The engagement equation: A model for understanding what drives physician engagement with data-driven clinical performance feedback

The engagement equation: A model for understanding what drives physician engagement with data-driven clinical performance feedback | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The engagement equation: A model for understanding what drives physician engagement with data-driven clinical performance feedback Laura Desveaux, Ruoxi Wang, Simona C Minotti, Benjamin Brown, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6769454/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Implementation Science Communications → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Clinical performance feedback (CPF) is widely used to support physician development and improve care. Yet, its impact remains limited by low voluntary engagement. This study sought to: (1) develop a theory-informed model outlining the key beliefs that shape physician engagement with CPF; (2) explore patterns of feedback orientation across physicians; and (3) understand how individual perceptions influence engagement with CPF. Methods: We used a cross-sectional, multi-method approach combining a survey and qualitative interviews with primary care physicians in Ontario, Canada. We validated a conceptual model using path analysis, explored heterogeneity in feedback orientation using latent profile analysis, and qualitatively examined how perceptions of CPF influenced engagement. Results: Survey results (n=206) supported a model in which engagement with CPF is shaped by five recipient characteristics: perceived need for change (change discrepancy), perceived value of CPF, confidence to act on feedback (feedback self-efficacy), belief that feedback is useful (feedback utility), and sense of responsibility to act (feedback accountability). Perceived utility mediated the effects of self-efficacy and value on accountability, and perceived need for change influenced value. Latent profile analysis identified three groups: physicians with high and balanced feedback orientation (n=32), moderate and balanced (n=143), and low feedback orientation with low self-efficacy (n=31). Interview findings (n=9) revealed two mindsets: physicians who saw value in CPF despite its limitations (engagers), and those who dismissed its relevance (non-engagers). These mindsets aligned with differences in value, utility, and accountability scores from the survey. Conclusions: Engagement with CPF is not one-size-fits-all. Physicians differ in how they appraise and act on feedback based on their beliefs about its relevance, usefulness, and their ability to act. CPF initiatives should explicitly link feedback to improved patient outcomes, focus on future actions, and provide clear, actionable guidance. Designing CPF that accounts for recipient heterogeneity is essential to realizing its full potential as an improvement strategy. Figures Figure 1 Figure 2 Figure 3 Contributions to the literature This study advances the science by specifying recipient-level determinants—change discrepancy, value, self-efficacy, utility, and accountability—that shape engagement with clinical performance feedback (CPF), a core implementation strategy. By specifying high-value determinants that drive engagement, these results reframe engagement not as a passive outcome, but as an active, theory-informed implementation target, shifting focus from design features alone to how CPF is appraised by recipients. These findings demonstrates that engagement is shaped by identifiable and modifiable beliefs, offering a practical model to tailor CPF initiatives to recipient profiles and increase the effectiveness of feedback interventions. Background Clinical performance feedback (CPF), also called audit and feedback, is a widely-used implementation strategy where a clinician’s performance is measured, compared to professional standards or target, and fed back to them to improve care outcomes at scale [ 1 ]. Ivers et al. [ 2 ] highlight a wide range in effect sizes for continuous outcomes, with an interquartile range of 1.3–26.1%, demonstrating that CPF can have a significant impact when implemented effectively. Unfortunately, this impact often remains untapped, due in part to low levels of clinician engagement [ 3 , 4 ]. Engagement in this context is defined as voluntarily receiving and actively reviewing personalized CPF. To date, CPF research has primarily focused on whether CPF works on average, however limited attention to engagement leaves opportunities for better patient outcomes unrealized. While it is known that characteristics of CPF – its design, content, and delivery – influence its effectiveness [ 2 , 5 , 6 ], far less is known about how these characteristics affect recipients to engage with it (or not) in the first place. Clinical Performance Feedback Intervention Theory (CP-FIT) outlines the necessary pathway for improvements in patient care, identifying three sets of variables that influence the feedback cycle: feedback variables, recipient characteristics, and the broader practice context [ 7 ]. Brown et al. [ 7 ] describe that positive attitudes with respect to feedback (i.e., views on the potential benefits) increased the likelihood of engagement. However, the full range of important recipient characteristics that might influence engagement with CPF and how they interact with one another has not yet been systematically explored. Simply put, the pathway(s) for engagement in CPF remain unclear, limiting our ability to target them with evidence-based approaches. In addition to suboptimal engagement, there is heterogeneity in responsiveness to CPF across recipients [ 8 , 9 ]. In the broader feedback literature, the perception of feedback delivery (e.g., whether the feedback is delivered in a non-judgemental manner) has a positive association with reactions to feedback among older recipients, while feedback quality (e.g., whether the feedback is relevant, specific, consistent, and detailed) has a positive association with reactions among younger recipients [ 10 ]. These insights highlight that recipient subgroups process feedback differently [ 10 , 11 ], but how perceptions of feedback affect engagement remains unclear. Preliminary evidence suggests that differences in perceived need for change, feedback attitudes (often called feedback orientation), and perceived value influence engagement among primary care physicians [ 12 ]. However, it is necessary to more fully understand which variables influence recipient perceptions of CPF and how they might interact to influence (or undermine) its effectiveness as an implementation strategy. The Necessity-Concerns Framework (NCF) [ 13 ] offers a useful lens to explore these dynamics as it emphasizes the balance individuals consider between the perceived benefits (necessity) and drawbacks (concerns) of an action. While the NCF provides a structured approach to understanding how patients approach medication decisions, the underlying model of cognitive appraisal may extend to CPF. Physicians, like patients, evaluate the benefits and risks of actions when deciding whether or not to engage with CPF, including how relevant it is to improving patient outcomes [ 14 ], the effort required to act on it [ 15 ], and potential consequences (e.g., criticism or increased workload) [ 16 ]. For example, if a physician sees data as a chance to improve patient care (necessity) but worries it might reveal a weakness or lead to extra work (concerns), the tension between these perceptions could determine whether they engage with the data. A deeper examination of how physicians cognitively appraise feedback—such as its relevance, specificity, and utility—might drive or deter their responsiveness to such performance data. Over the last decade, we have conducted a series of studies qualitatively exploring reactions to and experiences with CPF [ 12 , 17 – 19 ]. Together, this work suggested that the beliefs about feedback accountability influenced engagement with CPF with at least two antecedents: feedback self-efficacy and perceived feedback value. These studies highlight the importance influence physician characteristics have on the effectiveness of CPF and suggest that ‘engagement’ is the endpoint of a cognitive appraisal process following which physician recipients would decide whether to actively engage with or use CPF to identify performance gaps and make changes in their practice. To systematically understand the physician characteristics that influence engagement with CPF, this work sought to (1) develop an applied model to specify the constructs influencing engagement with CPF; (2) explore recipient heterogeneity in feedback orientation; and (3) qualitatively explore heterogeneity in perceptions of CPF and their impact on engagement. Given their influence on CPF, we build on prior work by clarifying recipient characteristics that influence engagement and how they interact. Insights from this work will support the identification of evidence-informed strategies to increase upstream engagement with CPF to realize its potential impact on patient outcomes. Model Development We began our review of the literature with CP-FIT [ 7 ] – a comprehensive theory of CPF in a healthcare context - which acknowledges the role of attitudes towards feedback as an upstream influence but does not specify whether these characteristics interact or how they influence perceptions of CPF. Next, we sought out a feedback-specific theory that focused on recipient beliefs. Linderbaum and Levy’s [ 20 ] Feedback Orientation Scale (FOS) centres around attitudes towards feedback, highlighting sub-domains of feedback self-efficacy, feedback utility, feedback accountability , and social awareness . While these sub-domains provide a strong foundation for understanding recipient beliefs that might influence engagement with CPF, two key knowledge gaps remained: the relationships between FOS sub-domains (given that most studies treat feedback orientation as a single concept [ 21 , 22 ]) and the influence of perceived value – a known antecedent to behavioural intention [ 23 , 24 ]. We then searched the literature related to FOS domains to generate hypotheses for testing. Feedback Self-Efficacy and Value as Antecedents of Feedback Accountability Linderbaum and Levy [ 20 ] defined feedback accountability as an individual’s perceived sense of responsibility for acting on feedback, making it a precursor to intention to act on specific feedback information as well as an outstanding driver of the behavioral response itself. Studies that have investigated the subdomains of feedback orientation conceptualized feedback accountability as a commitment to action , which is a more behaviour or action-oriented feature than the subdomains of feedback utility and feedback self-efficacy [ 25 , 26 ]. Feedback self-efficacy refers to individuals’ confidence in their ability to act on feedback [ 20 ]. The broader concept of self-efficacy has been widely investigated in various theories including Social Cognitive Theory [ 27 ], the Theory of Planned Behavior [ 24 ], and Social Cognitive Career Theory [ 28 ], with documented positive impact on both intention and behaviour. It has been conceptualized as an individual’s cognitive appraisal of their control over achievement and demonstrated as one of the two most important individual antecedents of behaviour from the lens of Control-Value Theory [ 29 , 30 ]. In the feedback orientation literature, Yang and Yang [ 25 ] linked feedback self-efficacy to feedback accountability and action (behaviour): individuals who have higher confidence in their ability to deal with feedback are more likely to feel responsible for acting on feedback, and accordingly, act on feedback more proactively. In addition to self-efficacy , Control-Value Theory [ 23 ] suggests value as the second individual antecedent of action. Value refers to individuals’ cognitive appraisal of a given action (in the case, the action of interacting with CPF reports) in terms of its personal relevance (i.e., whether the outcome of action matters to individuals). Value has been demonstrated as an important driver of action in Expectancy-Value Theory [ 31 ], the Theory of Planned Behavior [ 24 ], and Health Belief Model [ 32 ]. Self-efficacy and value have been shown to have independent influence on action (i.e., individuals can be best motivated if they have confidence in their ability to act and the corresponding outcome is important to them), highlighting the importance of investigating these two factors simultaneously [ 29 ]. Mediating Effect of Feedback Utility Feedback utility refers to individuals’ perceived usefulness of feedback in achieving desired outcomes [ 20 ]. Perceived usefulness has been widely investigated in the information system and marketing fields where it operates as a “medium attribute” (i.e., a characteristic of an information system). This is distinct from the construct of value that manifests as an individual/consumer characteristic [ 33 ]. In this way, value reflects a general belief about what matters to an individual, whereas perceived usefulness is a context-specific judgment about how well a particular tool or intervention aligns with those values. Prior studies have illustrated the mediating role of the perceived usefulness ( utility ) in explaining the effect of self-efficacy and value on intention. Alalwan et al. [ 34 ], Wang et al. [ 35 ] and Youn and Lee [ 36 ]. Alam et al. [ 37 ] and Han and Nam [ 38 ] further demonstrated the independent positive effects of self-efficacy and value on perceived usefulness as well as the positive effect of perceived usefulness on intention. In the feedback orientation literature, Frondozo and Yang [ 26 ] validated the mediating role of feedback utility in explaining the effect of feedback self-efficacy on feedback accountability : individuals who have higher confidence in their ability to deal with feedback tend to perceive feedback as more useful, and as a result, feel higher responsibility for acting on feedback. However, whether the mediating role of feedback utility also applies to explaining the effect of value on feedback accountability requires further investigation. These literature supported the following hypotheses: Hypothesis 1 Feedback self-efficacy has a positive relationship with feedback accountability via feedback utility in physicians. Hypothesis 2 Feedback value has a positive relationship with feedback accountability via feedback utility in physicians. Change Discrepancy as an Antecedent of Feedback Value Change discrepancy refers to individuals’ awareness of a situation that requires change (perceived need for change), which suggests a sense of relative prioritization and urgency [ 39 ]. Prior studies have demonstrated the crucial role of change discrepancy as a precursor to initial readiness for change and the corresponding change-related actions (i.e., initiation, persistence, and cooperative behaviours) [ 40 – 42 ]. A high sense of change discrepancy (derived from both perceiving discrepancy and evaluating such discrepancy as of high importance) triggers individuals’ information processing to understand the problem and explore opportunities for change [ 43 ]. Although it lacks empirical evidence in the feedback orientation literature, the logic of this argument stays sound: individuals must perceive the need for change before starting to consider feedback as a candidate solution to initiate behavioural change, developing positive feedback orientation based on cognitive appraisals that lead to a resulting action. As such, we integrate it as an antecedent of the cognitive appraisal of value in the following hypothesis: Hypothesis 3 Change discrepancy has a positive relationship with feedback value in physicians. Together, the above hypotheses form the basis of the resulting conceptual model (see Fig. 1 ). Methods Study Design We used a cross-sectional, multi-method approach to develop a model of physician attitudes and beliefs relating to CPF and to explore variation across key constructs. We achieved this in three sequential steps: (1) validate the model of engagement with CPF; (2) quantitatively explore recipient heterogeneity across key constructs; and (3) qualitatively explore recipient heterogeneity across key constructs. The protocol was approved by the Trillium Health Partners Research Ethics Board (ID # 1073). All participants provided informed consent prior to survey completion and interviews. Context and Setting This study was conducted in the primary care setting. In Ontario, Canada’s most populous province, most of the population (83%) has a primary care physician (PCP) as their first point of contact within the healthcare system [ 44 ], with approximately 14,500 physicians providing primary care services as of 2021 [ 44 – 46 ]. PCPs may work in various practice settings, such as solo practices, group practices, or Community Health Centres, and have different remuneration schedules available to them, ranging from fee-for-service to capitation to salary-based. Most PCPs in Ontario are paid through a blended capitation model, where they receive a fixed amount of money per patient registered to their practice based on factors such as age, sex, and health status (similar to capitation) as well as additional payments for specific services performed (similar to fee-for-service) [ 47 , 48 ]. Since 2013, all physicians in Ontario who voluntarily register received a CPF report from a government agency responsible for connecting and improving health care in the province. The report provides information about their practice, including prescribing and screening rates across a variety of clinical topics. Individual trends in each performance indicator are compared to peers and “change ideas” are included with links to educational resources and practice-based tools to support quality improvement. The CPF report is confidential and is not used for performance management. In 2017, four opioid prescribing indicators pertaining to non-palliative care patients were added to the report. A full mock report from 2020 is included in Additional File 1. Step 1: Model Validation Participant Recruitment Eligible participants included all registered PCPs (i.e., specialty of family medicine or general practice) in Ontario, Canada (n ≅ 14,700 as of 2018) [ 49 ]. Recruitment occurred via an online survey distributed via three channels: (1) the mailing lists of key organizations in primary care, (2) a targeted email from Ontario Health, the agency that oversees healthcare administration in the province of Ontario, to registered recipients of their MyPractice: Primary Care reports, and (3) social media channels (i.e., X). All recruitment materials provided a direct link to a letter of information and a subsequent electronic survey for those who consented. Survey participants were given the option to receive a $50 gift card for completing the survey. Data Collection The electronic survey included constructs from two validated and widely adopted scales: the Feedback Orientation Scale (FOS) and the Organizational Change Recipients’ Beliefs Scale (OCRBS). The FOS [ 20 , 21 ] measured feedback accountability, feedback self-efficacy, and feedback utility. We omitted the social awareness construct as CPF does not provide a means for recipients to understand how others view them. The OCRBS [ 39 , 50 ] measured perceptions on feedback value and change discrepancy. All items were measured on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree”. To better align with the context of survey participants, we modified the original OCRBS items to contextualize statements around the need for change (i.e., change discrepancy ) and perceived intrinsic and altruistic value that would come with receiving feedback (i.e., feedback value ) in the healthcare field. An overview of constructs and definitions can be found in Table 1 . Table 1 – Survey constructs measuring recipient characteristics Construct of interest Source Survey Operational Definition Feedback utility FOS The belief that CPF will improve the desired outcomes Feedback self-efficacy FOS Confidence in the ability to act on CPF Accountability FOS The sense of responsibility for acting on CPF Change discrepancy OCRBS The belief that something needs to improve Value OCRBS Whether CPF provides value in line with the perceived discrepancy CPF = Clinical perfromance feedback; FOS = Feedback Oreintation Scale; OCRBS = Organizational Change Recipients’ Beliefs Scale. Data Analysis Common method bias was assessed via multi-collinearity [ 51 ] and goodness-of-fit [ 52 ] and Confirmatory Factor Analysis was conducted to test the discriminant validity of the study constructs [ 53 ]. Partial Least Squares Path Modeling (PLS-PM) was then applied to test the conceptual model due to its capability of investigating complex latent construct models using a small sample size with non-parametric data [ 54 , 55 ]. The PLS-PM model was estimated to measure the direct effects, indirect effects, and total effects of the included constructs. Missing values were replaced by the mean value. To test the statistical significance of the PLS-PM estimates and provide the corresponding confidence intervals, a bootstrapping procedure based on 5000 resamples was applied [ 56 ]. Mediation analysis followed Zhao, Lynch and Chen [ 57 ]. All statistical analyses were performed in R 4.4.1, using packages including tidyLPA, lavaan, nnet, SEMinR, and plspm. Results After data cleaning the survey had 206 responses (see Table 2 for participant demographics). Results of all construct level Variance Inflation Factor scores were under the threshold of 3, indicating absence of multi-collinearity [ 56 ]. Goodness-of-fit was 0.49, higher than the cut-off of 0.36 to be considered as globally fit [ 58 ]. The original model yielded a satisfactory model fit to the sample data [ 54 , 59 , 60 ], including: Comparative Fit Index (CFI) > 0.90, Tucker–Lewis Index (TLI) > 0.90, Root Mean Square Error of Approximation (RMSEA) < 0.08, Standardized Root Mean Squared Residual (SRMR) < 0.08, and scaled 𝛘2/degree of freedom < 3 (see Additional File 2). Moreover, the original model had better model fit than the reduced models in all criteria. These analysis results indicated that the Common Method Bias was not an issue. After removing two items that did not meet the indicator loading threshold of 0.6 [ 61 ], the model demonstrated acceptable internal consistency, construct validity [ 62 ], and discriminant validity [ 56 , 63 ] (refer to Additional File 3 for detailed results). Table 2 Participant Characteristics F n % Gender Man 78 38.8 Woman 123 61.2 Missing 5 International graduate No 166 81.4 Yes 38 18.6 Missing 2 Practice year ≤5 years 62 30.1 ≥6 years 144 69.9 Work days/week ≤ 3 days 77 37.4 ≥4 days 129 62.6 Practice model Family Health Organization (FHO) 92 44.7 Other 114 55.3 Community size Urban centre 94 45.6 Smaller areas 112 54.4 Staff number within the organization ≤5 95 46.1 ≥6 111 53.9 Figure 2 presents the integrated model and Table 3 presents means, standard deviations, and inter-correlations of the study constructs. Table 3 Means, Standard Deviations, and Inter-Correlations of Study Constructs Mean SD C1 C2 C3 C4 C5 Change discrepancy (C1) 4.13 0.65 1 Feedback value (C2) 3.66 0.60 0.49 1 Feedback self-efficacy (C3) 3.72 0.61 0.20 0.35 1 Feedback utility (C4) 3.90 0.60 0.48 0.62 0.51 1 Feedback accountability (C5) 3.89 0.52 0.38 0.55 0.37 0.66 1 Hypothesis 1 Feedback self-efficacy has a positive relationship with feedback accountability via feedback utility. As Fig. 2 shows, we observed a significantly positive association between feedback self-efficacy and feedback utility ( β = 0.33, 95% CI: 0.21, 0.44), between feedback utility and feedback accountability ( β = 0.50, 95% CI: 0.38, 0.65), but no significant association between feedback self-efficacy and feedback accountability ( β = 0.03, 95% CI: -0.11, 0.16). Taking into consideration the significant indirect effect of feedback self-efficacy on feedback accountability via feedback utility ( β = 0.17, 95% CI: 0.10, 0.25), the mediation analysis revealed a full mediation effect of feedback utility , supporting Hypothesis 1 . Hypothesis 2 Feedback value has a positive relationship with feedback accountability via feedback utility. We observed a statistically significant positive association between feedback value and feedback utility ( β = 0.51, 95% CI: 0.40, 0.61) and between feedback utility and feedback accountability ( β = 0.50, 95% CI: 0.38, 0.65). The mediation analysis yielded a significant indirect effect of feedback value on feedback accountability via feedback utility ( β = 0.26, 95% CI: 0.17, 0.36). Considering a significant direct effect of feedback value on feedback accountability ( β = 0.22, 95% CI: 0.09, 0.35), the result indicated a complementary mediation effect of feedback utility , supporting Hypothesis 2 . Hypothesis 3 Change discrepancy has a positive relationship with feedback value. We observed a statistically significant positive relationship between change discrepancy and feedback value ( β = 0.48, 95% CI: 0.36, 0.59), supporting Hypothesis 3 . Step 2: Quantitatively explore recipient heterogeneity Participant Recruitment and Data Collection The same participants and electronic survey data described in Step 1 were used. Data Analysis We conducted a Latent Profile Analysis (LPA), a novel person-centered approach [ 64 ] not yet applied in the context of CPF, to identify the unique participant profiles that contribute to population heterogeneity in feedback orientation (i.e., feedback self-efficacy , feedback utility , and feedback accountability ). Confirmatory factor analysis (CFA) was used to validate the assumptions that the identified latent models were distinct and reliable [ 65 , 66 ]. To address non-normality and missing values, we used Maximum Likelihood with Robust standard errors estimation and Full Information Maximum Likelihood estimation, respectively. We extracted the factor scores of each construct following Scherer et al. [ 66 ] to generate more accurate estimates for each latent construct. Results After data cleaning the survey had 206 responses (see Table 2 ), with the majority being woman (61.2%), Canadian trained physicians (81.4%), with more than 5 years of practice experience (69.9%). Participants reported working more than 3 days/week (62.6%), with more than 5 colleagues (53.9%). Nearly half of the participants (45.6%) were based in an urban centre (see Table 1 for survey participant demographics). LPA identified three distinct class profiles (see Additional File 4), assigning 32 participants to Class 1, 143 participants to Class 2, and 31 participants to Class 3. Participants in Class 1 exhibited consistently higher factor scores than 0 (i.e., overall sample mean) on all profile indicators; those in Class 2 exhibited factor scores around 0 on all profile indicators; whereas those in Class 3 exhibited factor scores lower than 0, especially for feedback self-efficacy (see Fig. 3 ). In this case, we labelled these three classes as “high and balanced feedback orientation”, “moderate and balanced feedback orientation”, and “low feedback orientation (especially feedback self-efficacy )”, respectively. Table 4 reports the association of demographic and psychological characteristics with feedback orientation class membership. Using Class 2 as a reference group, we found that feedback value and change discrepancy were positively associated with the likelihood of being assigned to Class 1, e.g., for every one-unit increase in feedback value , the odds of being assigned to Class 1 increased by 508% compared to being assigned to Class 2. Feedback value was negatively associated with the likelihood of being assigned to Class 3 compared to being assigned to Class 2. Additionally, women were more likely to be assigned to Class 3, whereas those who worked in a practice model other than FHO were less likely to be assigned to Class 3 as compared to being assigned to Class 2. Table 4 Demographic and Psychological Characteristics Predicting Profile Membership of Feedback Orientation Class 1 vs Class 2 Class 3 vs Class 2 OR 95% confidence interval p OR 95% confidence interval p Feedback value (OCRBS) 6.08 2.11, 17.55 0.001 0.28 0.12, 0.67 0.005 Change discrepancy (OCRBS) 3.81 1.30, 11.20 0.015 1.62 0.77, 3.42 0.199 Gender (Woman vs Man) 0.49 0.18, 1.30 0.149 5.11 1.56, 16.71 0.007 International graduate (Yes vs No) 0.64 0.19, 2.19 0.477 1.63 0.46, 5.79 0.446 Practice year (≥ 6 vs ≤ 5) 1.15 0.37, 3.58 0.805 1.58 0.59, 4.27 0.363 Work days/week (≥ 4 vs ≤ 3) 1.16 0.39, 3.45 0.784 0.97 0.37, 2.55 0.945 Practice model (Other vs FHO) 0.70 0.26, 1.87 0.475 0.30 0.12, 0.77 0.013 Community size (Smaller size vs urban) 0.58 0.22, 1.52 0.266 0.85 0.34, 2.13 0.734 Staff number (≥ 6 vs ≤ 5) 0.39 0.14, 1.06 0.064 0.97 0.38, 2.48 0.952 N.B. Class 1: high and balanced feedback orientation, Class 2: moderate and balanced feedback orientation, Class 3: low feedback orientation (especially self-efficacy ). Step 3: Qualitatively explore recipient heterogeneity Participant Recruitment We used a convenience sampling approach for the interviews by including a question at the end of the electronic survey asking participants if they were interested in participating in a follow-up interview to discuss their beliefs and perspectives relating to CPF. Participants were advised they would be provided with a $150 honorarium for interview participation. We aimed to recruit PCPs eligible to receive the CPF report, whether they signed up for it or not. We contacted interested participants in consecutive order (i.e., starting with those that completed the survey first). We continued recruitment until no new insights emerged from the interviews. Data Collection A convenience sampling approach operationalized through the online survey generate a list of eligible participants. Semi-structured, exploratory interviews were informed by the survey constructs (Table 1 ) and sought to understand participant perspectives on CPF (see Additional File 5 for interview guide). Questions explored perspectives and beliefs relating to performance improvement in primary care, participant goals around quality of care, recipient and contextual factors perceived to influence engagement with CPF, and experiences with CPF. Example questions included ‘What role does feedback play for primary care physicians and the care they provide’ and ‘How does feedback contribute to your success (or not) as a primary care physician?’. As the focus of this study was on recipient attitudes and characteristics, data on contextual factors will be reported separately. Interviews were conducted virtually and were audio-recorded and transcribed verbatim by an independent third party. Data Analysis Transcripts were coded using MAXQDA. Interviews were analyzed using the framework method [ 67 , 68 ], with survey constructs and CP-FIT domains applied as pre-defined deductive codes. Inductive coding was applied when concepts were identified that did not fit within the definitions of pre-defined constructs. This approach supported exploration of potential interactions between individual attitudes and beliefs that may influence engagement with CPF. Once descriptive statements were generated to detail how each construct manifested in the data, we reviewed the transcripts to identify how these characteristics intersected with one another to generate themes. To support the framework analysis, qualitative findings on recipient factors influencing engagement were triangulated with participant survey results to achieve a deeper understanding of how these factors coalesce to shape the perceptions and beliefs of physicians [ 69 ]. This triangulation helped identify the characteristics (i.e., distinct attitudes and beliefs) that varied between individuals, enabling the development of typologies for participants who recognized the value of CPF and those who did not. These insights support the refinement of the conceptual model. Results Nine interviews were conducted with PCPs, ranging from 40 minutes to 62 minutes in duration. Additional File 6 outlines participant demographics and survey scores. Two distinct viewpoints emerged – one where participants did not see value in CPF, emphasizing its limitations and undermining perceived accountability (hereafter referred to as non-engagers), and one where participants acknowledged the limitations of CPF but still believed in its value and their accountability to improve (hereafter referred to as engagers). Simply put, some participants justified their limited use of the report because of its limitations (n = 5) while others perceived it as useful despite its limitations (n = 4). Four key perceptions distinguished these groups, which are described below in line with the constructs described above. Perceived need for change and value of CPF Physicians unanimously recognized the need for change within the primary care landscape, citing administrative burdens, deferred care due to the pandemic, and the departure of primary care physicians from the profession as factors that drive physician burnout (and therefore necessitate change). Despite a shared acknowledgement of the need for change, participants held varying perspectives on the role of CPF in informing change. Engagers tended to view CPF as a catalyst, actively considering themselves among those responsible to drive some of these changes. Conversely, non-engagers downplayed the role of CPF, emphasizing changes outside their professional scope as more impactful. “It’s like, patients are already getting reminders from [a regional support organization] for all three of these, breast, colon, cervical. Patients can book their own mammograms. Ideally, if I was going to change anything, why doesn't the province mail colon cancer screening kits directly to patients, instead of me having to tell the labs to do it? Some of these things are like, you’re telling me these numbers, so what? So I can call the patients and remind them they got a letter in the mail? Is that what you want me to do? Because you already have [the regional organization] doing this stuff, and now you’re asking me to do it as well.” P6, Non-Engager These varying perspectives aligned with participant scores on the OCRBS. While engagers and non-engagers scored similarly on the ‘Discrepancy’ factor, which measures the extent to which one feels that there are legitimate needs for change, non-engagers scored lower on the ‘Valence’ factor, which measures the extent to which one believes that they will benefit from engagement with CPF. Importantly, non-engagers stressed that their criticism of CPF was not directed at the general practice of collecting data to inform practice, but rather at its implementation within currently available reports providing feedback at the practice level. Perceived utility of CPF for addressing practice-related gaps Engagers perceived the report was helpful in supporting them in their role, including general improvement and identifying practice-related gaps and setting and monitoring goals aimed at addressing them. One physician explained: “There’s this quality improvement project we have to do every five years, so I believe that might have been part of the impetus and […] use this as one avenue in which to explore how we’re going to improve from a quality improvement lens.” P10 (Engager) Non-engagers were less likely to perceive this benefit, which was supported by their lower scores on the ‘Utility’ domain of the FOS. Many non-engagers expressed having alternative ways of identifying practice-related gaps, such as leveraging other sources of data-driven feedback (e.g., system level reports that identified specific patients that need action) or querying their electronic medical record (EMR) system. In fact, most non-engagers claimed that physicians should already have a sense of their performance relative to provincial averages, leading them to believe the report didn’t provide any novel insight. “I think [the CPF report] said I had more than the average opioid patients. I could have told them that. I run searches on my patients. We’ve done audits to look for people who are on benzodiazepines, or higher doses of opioids. I have a list that I go through every few months. So, it’s less helpful for me personally because I’m already on top of the things that I can track and change.” P1, Non-Engager Additionally, some non-engagers believed that CPF reports were primarily targeting low performers—a belief that stemmed from perceptions regarding the goal of CPF reports. Non-engagers perceived the reports as a way of standardizing practice patterns, increasing adherence to clinical guidelines, or reducing healthcare costs. These goals were viewed as distinct from facilitating practice improvement and conflicted with the way some physicians viewed their role. “I was thinking to myself, why would [a regional organization] generate these reports? What’s it for? And ultimately, I’m like, ‘Oh, it’s because they want to save healthcare dollars.’ Which is, ultimately, a good thing, right? […] But the guidelines from [the regional organization] are based on populations. When I practice, I’m only thinking of the patient in front of me. I’m rarely thinking of the population. So, if you say to someone, “I’m recommending a FIT test, but actually a colonoscopy is better. We just can’t afford to do it for everybody.” They’re going to want the colonoscopy, right? They don’t care that [FIT tests are] more economical for Ontario at a population level, and nor should I, to some degree.” P2, Non-Engager Perceived utility influences sense of accountability for engaging with and acting on CPF Engagers and non-engagers held varying perceptions regarding the level of accountability they feel for engaging with and acting upon CPF, which aligned with scores on the ‘Accountability’ domain of the FOS. Engagers often described accountability as linked to comprehensive care. “ There’s very little time, and there’s a lot of administrative duties. But part of my responsibility is to make sure I’m delivering high-quality care and trying to improve. So, I wouldn't say it’s extra admin stuff that I don’t have time for. I mean, to a degree, we have to make time. That’s part of the comprehensive care we're giving.” P9, Engager Two key factors emerged as shaping participants’ perceptions of accountability: the actionability of the feedback within the report and the broader organizational setting in which they worked. In terms of actionability, participants unanimously viewed CPF related to preventative cancer screening and hemoglobin A1C testing as more actionable compared to feedback related to opioid and antibiotic prescribing. Physicians viewed outside providers, such as pain clinics and walk-in clinics, as confounding opioid and antibiotic prescribing numbers, reducing participants’ sense of control and causing some to dismiss the data. This issue is compounded by the fact that practice-level CPF reports do not identify which patients have been prescribed these drugs and, unlike for preventative cancer screening, such information cannot be readily queried. “If I'm not able to tease out who it’s reflective of, in terms of actual patients, then it’s not useful. Because I actually don’t know. Let’s say, for example, it came back with something astronomically high, like 80 percent of your patients are prescribed opioids. OK, what’s actionable based on that? I guess I can ask patients more often if they're being prescribed opioids by other providers, but I can't affect other providers’ prescribing habits.” P6, Non-Engager Engagers were still more likely to find these indicators valuable, taking further lengths to contextualize and reflect on the data in ways that non-engagers did not. For example, one participant considered whether certain demographic characteristics of their patient population, such as rurality and proportion of patients with labor-intensive jobs, could be an influencing factor in the high prescribing rates of opioids. Another participant considered making changes to their practice to encourage patients to discuss opioid use, potentially paving the way for adjustments to treatment plans and deprescribing. Another described reflecting on their practice patterns in the context of broader patient outcomes: “The section on antibiotics, which is a new section, what was my antibiotic initiation rate […] In March was below average. And then I went above average again. So the number of antibiotic treatment episodes prescribed by me in the last six months was 16. […] They’re basically saying fewer antibiotics is better, but I would say the right number of antibiotics is better. It would be interesting to see how your hospitalization rate compares to your antibiotic prescribing rate. Because perhaps your antibiotics are preventing hospitalizations.” P15 (Engager) Simply put, engagers acknowledged the importance of contextualizing the report’s findings to derive insights while non-engagers wanted insights to be self-evident and readily actionable. Discussion Summary of Findings The results of this multi-method study provide insight into the factors that influence physician engagement with CPF and how they interact. Specifically, perceived change discrepancy (whether physicians think that something needs to improve), value (whether CPF provides value in line with the perceived discrepancy), self-efficacy (the physician’s confidence in their ability to act on CPF), utility (whether physicians believe CPF will improve the desired outcomes), and accountability (sense of responsibility for acting on CPF) influence engagement with CPF. These insights advance our understanding of what to target to address commonly cited engagement challenges with CPF [ 3 , 70 , 71 ], and enhances CPF theory (specifically CP-FIT) by providing complementary insights on which recipient characteristics engagement with CPF and how they interact. However, the way these constructs manifest varies across physicians, with individual differences shaping the degree to which CPF is perceived as beneficial or actionable. These results extend prior work by demonstrating that engagement is not automatic, but rather is influenced by physicians' interpretation of its relevance, usability, and their own capacity to act on it – and that these interpretations themselves vary across physicians. Comparison with Existing Literature The perceived value of CPF hinges on the belief that things need to change and its relevance to daily practice [ 12 , 18 ]. Our findings expand on best practice guidance for CPF [ 5 ] and build on prior work that has found physicians do not engage with CPF when they perceive no benefit [ 72 ] by specifying the factors that influence how physician’s assess benefit. Physicians must first recognize a need for change ( change discrepancy ) and view CPF as providing relevant, useful data. When these conditions are met, engagement is more likely. However, our analysis captures a single point in time, and further research is needed to understand how these perceptions evolve and what strategies effectively shift them. Our study extends beyond the focus on barriers to engagement with CPF to highlight actionable pathways to foster engagement by specifying high-value determinant to target. Physician self-assessment is often inaccurate [ 73 , 74 ], limiting opportunities for professional growth and emphasizing the need for external assessments to guide improvement. CPF provides a scalable way to help physicians identify habitual processes in their practice patterns. However, engagement is not uniform; physicians vary in their confidence (self-efficacy) and willingness to act on CPF, influenced by their past experiences, learning preferences, and perceived relevance of the data. Many physicians lack confidence (self-efficacy) in using such data effectively [ 12 , 18 ], aligning with the results of our latent profile analysis which showcase the role of self-efficacy in shaping feedback orientation. To effectively support improvement, CPF initiatives must simultaneously mitigate concerns and increase confidence in acting on the data, helping physicians to interpret population-level data and apply it to individual patients [ 12 , 18 ]. Kluger and Nir’s feedforward approach [ 75 ] may help by shifting the focus from critiquing past performance to guiding future actions, highlighting actionable next steps. This approach aligns with the NCF, emphasizing CPF’s benefits (e.g., growth and improvement) while reducing perceived judgment (concerns). By linking self-efficacy to engagement with CPF, our findings reinforce the importance of designing CPF strategies that explicitly build physician confidence and competence in data use, rather than assuming these exist at baseline. Implications for Practice CPF initiatives should be designed with key engagement factors in mind, and implementation efforts should proactively communicate how CPF meets these needs. For example, specifying how the data aligns to physician values, including aspirations relating to clinical care, communication goals, and protecting time with patients downstream [ 72 , 76 ], may increase perceived benefit. Those designing CPF should consider the explicit cues that would encourage a new course of action (i.e., engagement with CPF or a practice change). Situational strength—the contextual cues that shape behavior—plays a key role in influencing whether physicians change course [ 77 ]. In the context of CPF, high clarity provides explicit guidance on actions that improve outcomes, while high consequences reinforce the impact of these actions on patient care. Delivering CPF with future-oriented goals and actionable guidance can make it more constructive, reframing it as a learning opportunity rather than a critique. Combining this approach with facilitated coaching conversations can help physicians create concrete plans to act on CPF, closing the oft-cited gap between intention and action [ 12 , 17 , 18 , 78 , 79 ]. These conversations should cue physicians to identify potential changes and guide them to making those changes routine practice, ultimately enabling them to intuitively respond to CPF without relying on effortful interpretation amid competing demands [ 80 ]. Taken together, these strategies provide a structured and evidence-informed roadmap for improving CPF engagement to better position it for impact on clinical practice. Limitations Primary care physicians in Ontario, Canada receive numerous CPF reports from various sources, therefore it is possible that preconceived notions of what feedback is could have affected the measurement of the related constructs. Future work should explore existing mental models around feedback in a healthcare context to identify whether and how a new model of feedback is needed in healthcare. Additional work is needed to explore whether the findings in this study extend beyond the current sample (primary care physicians), including other medical specialties and clinicians who receive CPF. Given the widespread and increasing frequency with which data is provided to physicians, future work should explore whether and how these beliefs may systematically affect physicians’ judgements and decisions as it relates to using data more generally to identify and drive improvements in their practice should be explored in future work. The results from this study can be used as the basis for interventional studies, including those that engage physicians in co-designing CPF, to support better specification of how to address value, utility, confidence, and accountability in practice. Conclusions This study offers novel insights into the cognitive appraisal process that influences whether physicians voluntarily engage with CPF. The evidence-based design of CPF has the potential to support physician development with an emphasis on improving patient outcomes. Specification of the factors informing CPF appraisal support the personalization of performance feedback strategies – akin to personalizing care for the patient in front of you. Based on our findings, we propose that CPF initiatives explicitly state how CPF supports better patient outcomes, maintain a future-focus, and provide clear instructions about the behaviour(s) leading to improved outcomes. Recognizing that ‘intention is everything’ [ 72 ], CPF initiatives must clearly communicate how they support professional growth and improve patient outcomes in order to realize their promise of supporting health system improvement at scale. Abbreviations CFI Comparative Fit Index CPF Clinical Performance Feedback CP-FIT Clinical Performance Feedback Intervention Theory FOS Feedback Oreintation Scale NCF Necessity-Concerns Framework OCRBS Organizational Change Recipients’ Beliefs Scale PCP Primary care physician RMSEA Root Mean Square Error of Approximation SRMR Standardized Root Mean Squared Residual TLI Tucker–Lewis Index Declarations Ethics approval and consent to participate This study was approved by the Trillium Health Partners Research Ethics Board (Protocol ID #1073). All participants provided informed consent prior to participation in the survey and interviews. Consent for publication Not applicable. The manuscript does not contain any individual person’s data in any form. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to participant confidentiality, but may be available from the corresponding author on reasonable request and with appropriate ethical approvals. Competing interests The authors declare that they have no competing interests. Funding This study was supported by funding from the Canadian Institutes of Health Research (PJT 178046). The funder had no role in the design of the study, data collection, analysis, interpretation of data, or writing of the manuscript. Authors' contributions LD conceived of the study and led the design, analysis, and manuscript preparation. RW led the quantitative analyses and supported model development, with the support of SCM. BT led the qualitative data collection and analyses with the support of LD. NMI, BB, AH, FR, AV, GR, and MT contributed to data interpretation and manuscript revisions. All authors reviewed and approved the final manuscript. Acknowledgements The authors wish to thank the physicians who generously shared their time and perspectives, and the team at Ontario Health for supporting survey distribution. We also acknowledge the support of research staff at the Institute for Better Health for project coordination and data management. References Harrison R, Hinchcliff RA, Manias E, Mears S, Heslop D, Walton V, et al. Can feedback approaches reduce unwarranted clinical variation? A systematic rapid evidence synthesis. BMC Health Serv Res. 2020;20(1):40. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database of Systematic Reviews. 2012; 2012(7):CD000259-CD59. McCleary N, Desveaux L, Presseau J, Reis C, Witteman HO, Taljaard M, et al. Engagement is a necessary condition to test audit and feedback design features: results of a pragmatic, factorial, cluster-randomized trial with an embedded process evaluation. Implement Sci. 2023;18(1):13. Ivers NM, Taljaard M, Giannakeas V, Reis C, Mulhall CL, Lam JMC, et al. Effectiveness of confidential reports to physicians on their prescribing of antipsychotic medications in nursing homes. Implement Sci Commun. 2020;1(1):30. Brehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, et al. Practice feedback interventions: 15 suggestions for optimizing effectiveness. Ann Intern Med. 2016;164(6):435–41. O'Connor DA, Glasziou P, Maher CG, McCaffery KJ, Schram D, Maguire B, et al. Effect of an individualized audit and feedback intervention on rates of musculoskeletal diagnostic imaging requests by Australian general practitioners: a randomized clinical trial. JAMA. 2022;328(9):850–60. Brown B, Gude WT, Blakeman T, van der Veer SN, Ivers N, Francis JJ, et al. Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci. 2019;14(1):40. Trent SA, Havranek EP, Ginde AA, Haukoos JS. Effect of audit and feedback on physician adherence to clinical practice guidelines for pneumonia and sepsis. Am J Med Qual. 2019;34(3):217–25. Trietsch J, van Steenkiste B, Grol R, Winkens B, Ulenkate H, Metsemakers J, et al. Effect of audit and feedback with peer review on general practitioners' prescribing and test ordering performance: a cluster-randomized controlled trial. BMC Fam Pract. 2017;18(1):53. Wang M, Burlacu G, Truxillo D, James K, Yao X. Age differences in feedback reactions: the roles of employee feedback orientation on social awareness and utility. J Appl Psychol. 2015;100(4):1296–308. Braddy PW, Sturm RE, Atwater LE, Smither JW, Fleenor JW. Validating the Feedback Orientation Scale in a leadership development context. Group Organ Manage. 2013;38(6):690–716. Desveaux L, Ivers NM, Devotta K, Ramji N, Weyman K, Kiran T. Unpacking the intention to action gap: a qualitative study understanding how physicians engage with audit and feedback. Implement Sci. 2021;16(1):19. Phillips LA, Diefenbach MA, Kronish IM, Negron RM, Horowitz CR. The Necessity-Concerns Framework: a multidimensional theory benefits from multidimensional analysis. Ann Behav Med. 2014;48(1):7–16. Veloski J, Boex JR, Grasberger MJ, Evans A, Wolfson DB. Systematic review of the literature on assessment, feedback and physicians' clinical performance: BEME Guide 7. Med Teach. 2006;28(2):117–28. Sargeant J, Bruce D, Campbell CM. Practicing physicians' needs for assessment and feedback as part of professional development. J Continuing Educ Health Professions. 2013;33:S54–62. Ferguson J, Wakeling J, Bowie P. Factors influencing the effectiveness of multisource feedback in improving the professional practice of medical doctors: a systematic review. BMC Med Educ. 2014;14:76. Desveaux L, Nguyen MD, Ivers NM, Devotta K, Upshaw T, Ramji N, et al. Snakes and ladders: a qualitative study understanding the active ingredients of social interaction around the use of audit and feedback. Translational Behav Med. 2023;13(5):316–26. Ivers N, Barnsley J, Upshur R, Tu K, Shah B, Grimshaw J, et al. My approach to this job is … one person at a time Perceived discordance between population-level quality targets and patient-centred care. Canadian Family Physician. 2014; 60(3):258 – 66. Rouleau G, Reis C, Ivers N, Desveaux L. Characterizing the gaps between best-practice implementation strategies and real-world implementation: qualitative study among family physicians who engaged with audit and feedback reports. JMIR Hum Factors. 2023;10:e38736. Linderbaum BA, Levy PE. The development and validation of the Feedback Orientation Scale (FOS). J Manag. 2010;36(6):1372–405. Dahling JJ, Chau SL, O'Malley A. Correlates and consequences of feedback orientation in organizations. J Manag. 2012;38(2):531–46. Smither JW, London M, Reilly RR. Does performance improve following multisource feedback? A theoretical model, meta-analysis, and review of empirical findings. Pers Psychol. 2005;58(1):33–66. Pekrun R. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational Psychol Rev. 2006;18(4):315–41. Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. Yang L, Yang M. Exploring the power of teacher feedback in Chinese students: testing the relationships between students’ feedback beliefs and student engagement. In: Yang L, Yang M, editors. Asian Education Miracles. London: Routledge; 2019. pp. 155 – 73. Frondozo CE, Yang L. Changing external feedback to learning opportunities: a study on Filipino university students' feedback orientation. In: Yan Z, Yang L, editors. Assessment as Learning:Maximising Opportunities for Student Learning and Achievement. New York, NY: Routledge; 2022. pp. 190–205. Bandura A. Social cognitive theory: an agentic perspective. Ann Rev Psychol. 2001;52:1–26. Lent RW, Brown SD. Social Cognitive Career Theory at 25: empirical status of the interest, choice, and performance models. J Vocat Behav. 2019;115:103316. Luo WS, Ng PT, Lee K, Aye KM. Self-efficacy, value, and achievement emotions as mediators between parenting practice and homework behavior: a control-value theory perspective. Learn Individual Differences. 2016;50:275–82. Roick J, Ringeisen T. Self-efficacy, test anxiety, and academic success: A longitudinal validation. Int J Educational Res. 2017;83:84–93. Eccles JS, Adler TF, Futterman R, Goff SB, Kaczala CM, Meece JL, et al. Expectancies, values and academic behaviors. In: Spence JT, editor. Achievement and achievement motives. San Francisco, CA: W. H. Freeman; 1983. pp. 75–146. Rosenstock IM. Historical origins of health belief model. Health Educ Monogr. 1974;2(4):328–35. Ayalew M, Zewdie S. What factors determine the online consumer behavior in this digitalized world? A systematic literature. Human Behavior and Emerging Technologies. 2022; 2022. Alalwan AA, Dwivedi YK, Rana NP, Williams MD. Consumer adoption of mobile banking in Jordan examining the role of usefulness, ease of use, perceived risk and self-efficacy. J Enterp Inform Manage. 2016;29(1):118–39. Wang C, Chou S-cT, Chang H-C. Examining the impacts of perceived value and perceived quality on users' intention to join web 2.0 communities. In: Proceedings of the 11th International Conference on Electronic Commerce. Taipei, Taiwan: Association for Computing Machinery; 2009. pp. 329–34. Youn S-y, Lee K-H. Proposing value-based technology acceptance model: testing on paid mobile media service. Fashion Textiles. 2019;6(1):13. Alam SS, Masukujjaman M, Ahmad M, Jaffor R. Acceptance of online distance learning (ODL) among students: mediating role of utilitarian and hedonic value. Educ Inform Technol. 2023;28(7):8503–36. Han S, Nam SI. Creating supportive environments and enhancing personal perception to bridge the digital divide among older adults. Educ Gerontol. 2021;47(8):339–52. Armenakis AA, Bernerth JB, Pitts JP, Walker HJ. Organizational Change Recipients' Beliefs Scale: development of an assessment instrument. J Appl Behav Sci. 2007;43(4):481–505. Weiner BJ. A theory of organizational readiness for change. Implement Sci. 2009;4:67. Holt DT, Helfrich CD, Hall CG, Weiner BJ. Are you ready? How health professionals can comprehensively conceptualize readiness for change. J Gen Intern Med. 2010;25:50–5. Rafferty AE, Minbashian A. Cognitive beliefs and positive emotions about change: Relationships with employee change readiness and change-supportive behaviors. Hum Relat. 2019;72(10):1623–50. George JM, Jones GR. Towards a process model of individual change in organizations. Hum Relat. 2001;54(4):419–44. Statistics Canada. 2020; Primary health care providers, 2019 [ http://www150.statcan.gc.ca/n1/pub/82-625-x/2020001/article/00004-eng.htm] . Accessed March 24 2025. Kiran T, Green ME, Wu CFY, Kopp A, Latifovic L, Frymire E, et al. Family physicians stopping practice during the COVID-19 pandemic in Ontario, Canada. Ann Fam Med. 2022;20(5):460–63. Howard M, Wu F, Kiran T, Frymire E, Lapointe-Shaw L, Jaakkimainen L, et al. The impact of the COVID 19 pandemic on continuity of care with Ontario family physicians. Ann Fam Med. 2023;21(Supplement 1):3671. Glazier RH, Green ME, Frymire E, Kopp A, Hogg W, Premji K, et al. Do incentive payments reward the wrong providers? A study of primary care reform in Ontario, Canada. Health Aff. 2019;38(4):624–32. Park B, Gold SB, Bazemore A, Liaw W. How evolving United States payment models influence primary care and its impact on the quadruple aim. J Am Board Fam Med. 2018;31(4):588–604. Canadian Medical Association. 2018; Number of physicians by province/territory and specialty, Canada, 2018 [ https://www.cma.ca/sites/default/files/pdf/Physician%20Data/01-physicians-by-specialty-province-e.pdf] . Accessed January 02 2025. Van Praet E, Van Leuven S. When change readiness spirals down: a mixed-method case study of change communication at a European government agency. Int J Bus Communication. 2022;59(1):104–25. Modaresnezhad M, Andrews MC, Mesmer-Magnus J, Viswesvaran C, Deshpande S. Anxiety, job satisfaction, supervisor support and turnover intentions of mid-career nurses: a structural equation model analysis. J Nurs Adm Manag. 2021;29(5):931–42. Mikalef P, Krogstie J, Pappas IO, Pavlou P. Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manag. 2020;57(2):103169. Eva N, Meacham H, Newman A, Schwarz G, Tham TL. Is coworker feedback more important than supervisor feedback for increasing innovative behavior? Hum Resour Manag. 2019;58(4):383–96. Dash G, Paul J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol Forecast Soc Chang. 2021;173:121092. Tenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C. PLS path modeling. Comput Stat Data Anal. 2005;48(1):159–205. Hair J, Alamer A. Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: guidelines using an applied example. Res Methods Appl Linguistics. 2022;1(3):100027. Zhao XS, Lynch JG, Chen QM. Reconsidering Baron and Kenny: myths and truths about mediation analysis. J Consum Res. 2010;37(2):197–206. Wetzels M, Odekerken-Schroder G, van Oppen C. Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 2009;33(1):177–95. Santini ZI, Jose PE, Cornwell EY, Koyanagi A, Nielsen L, Hinrichsen C, et al. Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. Lancet Public Health. 2020;5(1):E62–70. Weston R, Gore PA. A brief guide to structural equation modeling. Couns Psychol. 2006;34(5):719–51. MacCallum RC, Widaman KF, Zhang SB, Hong SH. Sample size in factor analysis. Psychol Methods. 1999;4(1):84–99. Hair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Soumya R. Partial Least Squares Structural Equation Modeling (PLS-SEM) using R: a workbook. Cham: Springer; 2021. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43(1):115–35. Marsh HW, Lüdtke O, Trautwein U, Morin AJS. Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Struct Equation Modeling-a Multidisciplinary J. 2009;16(2):191–225. Spurk D, Hirschi A, Wang M, Valero D, Kauffeld S. Latent profile analysis: a review and how to guide of its application within vocational behavior research. J Vocat Behav. 2020;120:103445. Scherer R, Howard SK, Tondeur J, Siddiq F. Profiling teachers' readiness for online teaching and learning in higher education: who's ready? Comput Hum Behav. 2021;118:106675. Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1):117. Smith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res. 2011;18(2):52–62. Hopf YM, Francis J, Helms PJ, Haughney J, Bond C. Core requirements for successful data linkage: an example of a triangulation method. BMJ Open. 2016;6(10):e011879. Ivers NM, Grimshaw JM, Jamtvedt G, Flottorp S, O’Brien MA, French SD, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534–41. Daneman N, Lee S, Bai HM, Bell CM, Bronskill SE, Campitelli MA, et al. Behavioral nudges to improve audit and feedback report opening among antibiotic prescribers: a randomized controlled trial. Open Forum Infect Dis. 2022;9(5):ofac111. Levoy E, Vilendrer S, Dang R, Nasr AS, Luu J, Tawfik D, et al. Physician perspectives of clinical performance feedback and impact on well-being: a qualitative exploration. BMJ Open. 2024;14(11):e082726. Davis DA, Mazmanian PE, Fordis M, Van Harrison R, Thorpe KE, Perrier L. Accuracy of physician self-assessment compared with observed measures of competence: a systematic review. JAMA. 2006;296(9):1094–102. Nayar SK, Musto L, Baruah G, Fernandes R, Bharathan R. Self-assessment of surgical skills: a systematic review. J Surg Educ. 2020;77(2):348–61. Kluger AN, Nir D. The feedforward interview. Hum Resource Manage Rev. 2010;20(3):235–46. Agarwal SD, Pabo E, Rozenblum R, Sherritt KM. Professional dissonance and burnout in primary care a qualitative study. JAMA Intern Med. 2020;180(3):395–401. Meyer RD, Dalal RS, Hermida R. A review and synthesis of situational strength in the organizational sciences. J Manag. 2010;36(1):121–40. Saddawi-Konefka D, Schumacher DJ, Baker KH, Charnin JE, Gollwitzer PM. Changing physician behavior with implementation intentions: closing the gap between intentions and actions. Acad Med. 2016;91(9):1211–16. Mastikhina L, Li J, Taplin J, Egunsola O, Dowsett L, Noseworthy T, et al. Large-scale implementation of physician audit and feedback. University of Calgary Health Technology Assessment Unit. Produced for the Strategic Clinical Networks, Alberta Health Services. The Health Technology Assessment Unit, University of Calgary; 2020. pp. 1–25. Potthoff S, Presseau J, Sniehotta FF, Johnston M, Elovainio M, Avery L. Planning to be routine: habit as a mediator of the planning-behaviour relationship in healthcare professionals. Implement Sci. 2017;12:24. Supplementary Files AdditionalFile1SampleReport.pdf AdditionalFile2ConfirmatoryFactorAnalysis.docx AdditionalFile3ModelDiagnostics.docx AdditionalFile4LPAresults.docx AdditionalFile5IMPaCTInterviewGuide.docx AdditionalFile6InterviewParticipantData.docx STROBEChecklist.docx Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Implementation Science Communications → Version 1 posted Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 29 May, 2025 First submitted to journal 28 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6769454","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475160083,"identity":"44160125-88cc-413f-9103-3b0058b0e3e7","order_by":0,"name":"Laura Desveaux","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACPgjFDOPbMBgQ0sKGpiVNgmQth4nQwn7G7MMPBms5+RnJBz/ztp2vM5duYASK4NHCk2M8s4ch3djgRlqyNM+Z2xKWcw4wS/bgdViOMQMPw+HEDTxnDKR5Km5LGNxIYAOK4NHC/8aY8Q/D4fr5Pec//+YxOAfWAhTBo0Uix5gZaEsCw/EeNqAtB8BamPHaIvGsmFnGIN1ww/E2M8s5Z5IlN9w52Cwtg0cLP3/yZsY3Fdby8s3Mj2+8bbPjN7jdfPDjGzxaIAAlLiQYGwhqQAMSpGoYBaNgFIyC4Q4AguxC+OwDLOwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3429-1865","institution":"Trillium Health Partners","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Desveaux","suffix":""},{"id":475160084,"identity":"783ae415-27dc-4111-8505-d79c7d768aa9","order_by":1,"name":"Ruoxi Wang","email":"","orcid":"","institution":"Trillium Health Centre West Toronto: Queensway Health Centre","correspondingAuthor":false,"prefix":"","firstName":"Ruoxi","middleName":"","lastName":"Wang","suffix":""},{"id":475160085,"identity":"b5be982f-4eb9-4909-89ca-ccafdee19c76","order_by":2,"name":"Simona C Minotti","email":"","orcid":"","institution":"Trillium Health Partners","correspondingAuthor":false,"prefix":"","firstName":"Simona","middleName":"C","lastName":"Minotti","suffix":""},{"id":475160086,"identity":"0ac74f25-ced0-4496-b866-e4e198972c1a","order_by":3,"name":"Benjamin Brown","email":"","orcid":"","institution":"Manchester University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Brown","suffix":""},{"id":475160087,"identity":"4e22dd76-d014-4046-936c-f82caf0eaa57","order_by":4,"name":"Alexandra Harris","email":"","orcid":"","institution":"Unity Health Toronto","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Harris","suffix":""},{"id":475160088,"identity":"217b5380-0c17-4909-a069-57212f7c627b","order_by":5,"name":"Amol Verma","email":"","orcid":"","institution":"Unity Health Toronto","correspondingAuthor":false,"prefix":"","firstName":"Amol","middleName":"","lastName":"Verma","suffix":""},{"id":475160089,"identity":"7721dca3-1f21-4147-ac75-47438077e1ae","order_by":6,"name":"Genevieve Rouleau","email":"","orcid":"","institution":"Universite du Quebec en Outaouais","correspondingAuthor":false,"prefix":"","firstName":"Genevieve","middleName":"","lastName":"Rouleau","suffix":""},{"id":475160090,"identity":"3c7ec471-023c-4e18-a99b-637f91ab759c","order_by":7,"name":"Mina Tadrous","email":"","orcid":"","institution":"University of Toronto Leslie Dan Faculty of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Tadrous","suffix":""},{"id":475160091,"identity":"07c97389-7e20-43fa-9333-4d85baa0065a","order_by":8,"name":"Braeden Terpou","email":"","orcid":"","institution":"Trillium Health Partners","correspondingAuthor":false,"prefix":"","firstName":"Braeden","middleName":"","lastName":"Terpou","suffix":""},{"id":475160092,"identity":"e1a55ea7-0dbc-42d0-b4e0-d557c3de642f","order_by":9,"name":"Noah M Ivers","email":"","orcid":"","institution":"Women's College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Noah","middleName":"M","lastName":"Ivers","suffix":""}],"badges":[],"createdAt":"2025-05-28 15:18:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6769454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6769454/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43058-025-00819-5","type":"published","date":"2025-12-11T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85615589,"identity":"af37b919-4bb2-4a37-9180-cb43b70d3d3d","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated conceptual model of the recipient characteristics that influence engagement with Clinical Performance Feedback\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/4665fc39a686aaf3f0f6e2ff.png"},{"id":85615590,"identity":"07fc56dc-f91b-4b38-a6bf-67ab3daa9ef1","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated relationships within the integrated model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.B. Numbers in parentheses indicate 95% confidence intervals. Dashed arrows indicate effects that are not statistically significant.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/ead9eb93a6b839eaa9caaf02.png"},{"id":85616891,"identity":"eb21942f-f42a-4f05-89c1-78d8c21aaaf5","added_by":"auto","created_at":"2025-06-29 14:40:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLatent profiles of feedback orientation among primary care physicians\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/d723e150d9517305078f2fa5.png"},{"id":98243671,"identity":"cc64bb04-62ff-449b-9c9c-4441d4897f3a","added_by":"auto","created_at":"2025-12-15 16:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1676752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/9875d6ee-c02e-487c-b197-24acc6fb848b.pdf"},{"id":85615594,"identity":"2015f7c0-8848-43cb-b62d-12c949976bc3","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2211953,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1SampleReport.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/9b3e0022a97bf132ee042eb9.pdf"},{"id":85615591,"identity":"04738d05-4e03-47c1-ac09-343b6b730280","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21290,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2ConfirmatoryFactorAnalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/2d61d1305268901d963cfdfd.docx"},{"id":85617955,"identity":"d2c1874d-774c-4186-b793-6300725d077f","added_by":"auto","created_at":"2025-06-29 14:48:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":42505,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile3ModelDiagnostics.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/1c66594fc829ed4aea74130c.docx"},{"id":85615597,"identity":"cb4ef322-9a24-45cc-8694-c33f7798d441","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":24176,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile4LPAresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/9d05f63a4bf72999ca50494e.docx"},{"id":85615602,"identity":"fb514716-d848-44b1-8b77-cb422a18533e","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21470,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile5IMPaCTInterviewGuide.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/6cc77c5e1308a471e68fb59e.docx"},{"id":85615599,"identity":"3e6c707e-7ef1-4fd1-a888-6a4bb54e1afc","added_by":"auto","created_at":"2025-06-29 14:32:04","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":23606,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile6InterviewParticipantData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/4f99e5877f1677ed4664b451.docx"},{"id":85616898,"identity":"b3a307a7-bf9d-4717-a9d7-fc22b4f64756","added_by":"auto","created_at":"2025-06-29 14:40:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":34001,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769454/v1/88fc714326987a2dbc9f5218.docx"}],"financialInterests":"","formattedTitle":"The engagement equation: A model for understanding what drives physician engagement with data-driven clinical performance feedback","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eThis study advances the science by specifying recipient-level determinants\u0026mdash;change discrepancy, value, self-efficacy, utility, and accountability\u0026mdash;that shape engagement with clinical performance feedback (CPF), a core implementation strategy.\u003c/li\u003e\n \u003cli\u003eBy specifying high-value determinants that drive engagement, these results reframe engagement not as a passive outcome, but as an active, theory-informed implementation target, shifting focus from design features alone to how CPF is appraised by recipients.\u003c/li\u003e\n \u003cli\u003eThese findings demonstrates that engagement is shaped by identifiable and modifiable beliefs, offering a practical model to tailor CPF initiatives to recipient profiles and increase the effectiveness of feedback interventions.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eClinical performance feedback (CPF), also called audit and feedback, is a widely-used implementation strategy where a clinician\u0026rsquo;s performance is measured, compared to professional standards or target, and fed back to them to improve care outcomes at scale [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ivers et al. [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e] highlight a wide range in effect sizes for continuous outcomes, with an interquartile range of 1.3\u0026ndash;26.1%, demonstrating that CPF can have a significant impact when implemented effectively. Unfortunately, this impact often remains untapped, due in part to low levels of clinician engagement [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Engagement in this context is defined as voluntarily receiving and actively reviewing personalized CPF. To date, CPF research has primarily focused on whether CPF works on average, however limited attention to engagement leaves opportunities for better patient outcomes unrealized.\u003c/p\u003e\n\u003cp\u003eWhile it is known that characteristics of CPF \u0026ndash; its design, content, and delivery \u0026ndash; influence its effectiveness [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e], far less is known about how these characteristics affect recipients to engage with it (or not) in the first place. Clinical Performance Feedback Intervention Theory (CP-FIT) outlines the necessary pathway for improvements in patient care, identifying three sets of variables that influence the feedback cycle: feedback variables, recipient characteristics, and the broader practice context [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Brown et al. [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] describe that positive attitudes with respect to feedback (i.e., views on the potential benefits) increased the likelihood of engagement. However, the full range of important recipient characteristics that might influence engagement with CPF and how they interact with one another has not yet been systematically explored. Simply put, the pathway(s) for engagement in CPF remain unclear, limiting our ability to target them with evidence-based approaches.\u003c/p\u003e\n\u003cp\u003eIn addition to suboptimal engagement, there is heterogeneity in responsiveness to CPF across recipients [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the broader feedback literature, the perception of feedback delivery (e.g., whether the feedback is delivered in a non-judgemental manner) has a positive association with reactions to feedback among older recipients, while feedback quality (e.g., whether the feedback is relevant, specific, consistent, and detailed) has a positive association with reactions among younger recipients [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. These insights highlight that recipient subgroups process feedback differently [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], but how perceptions of feedback affect engagement remains unclear. Preliminary evidence suggests that differences in perceived need for change, feedback attitudes (often called feedback orientation), and perceived value influence engagement among primary care physicians [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, it is necessary to more fully understand which variables influence recipient perceptions of CPF and how they might interact to influence (or undermine) its effectiveness as an implementation strategy.\u003c/p\u003e\n\u003cp\u003eThe Necessity-Concerns Framework (NCF) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] offers a useful lens to explore these dynamics as it emphasizes the balance individuals consider between the perceived benefits (necessity) and drawbacks (concerns) of an action. While the NCF provides a structured approach to understanding how patients approach medication decisions, the underlying model of cognitive appraisal may extend to CPF. Physicians, like patients, evaluate the benefits and risks of actions when deciding whether or not to engage with CPF, including how relevant it is to improving patient outcomes [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], the effort required to act on it [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], and potential consequences (e.g., criticism or increased workload) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, if a physician sees data as a chance to improve patient care (necessity) but worries it might reveal a weakness or lead to extra work (concerns), the tension between these perceptions could determine whether they engage with the data. A deeper examination of how physicians cognitively appraise feedback\u0026mdash;such as its relevance, specificity, and utility\u0026mdash;might drive or deter their responsiveness to such performance data.\u003c/p\u003e\n\u003cp\u003eOver the last decade, we have conducted a series of studies qualitatively exploring reactions to and experiences with CPF [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Together, this work suggested that the beliefs about \u003cem\u003efeedback accountability\u003c/em\u003e influenced engagement with CPF with at least two antecedents: \u003cem\u003efeedback self-efficacy\u003c/em\u003e and perceived \u003cem\u003efeedback value.\u003c/em\u003e These studies highlight the importance influence physician characteristics have on the effectiveness of CPF and suggest that \u0026lsquo;engagement\u0026rsquo; is the endpoint of a cognitive appraisal process following which physician recipients would decide whether to actively engage with or use CPF to identify performance gaps and make changes in their practice. To systematically understand the physician characteristics that influence engagement with CPF, this work sought to (1) develop an applied model to specify the constructs influencing engagement with CPF; (2) explore recipient heterogeneity in feedback orientation; and (3) qualitatively explore heterogeneity in perceptions of CPF and their impact on engagement. Given their influence on CPF, we build on prior work by clarifying recipient characteristics that influence engagement and how they interact. Insights from this work will support the identification of evidence-informed strategies to increase upstream engagement with CPF to realize its potential impact on patient outcomes.\u003c/p\u003e\n\u003ch3\u003eModel Development\u003c/h3\u003e\n\u003cp\u003eWe began our review of the literature with CP-FIT [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] \u0026ndash; a comprehensive theory of CPF in a healthcare context - which acknowledges the role of attitudes towards feedback as an upstream influence but does not specify whether these characteristics interact or how they influence perceptions of CPF. Next, we sought out a feedback-specific theory that focused on recipient beliefs. Linderbaum and Levy\u0026rsquo;s [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] Feedback Orientation Scale (FOS) centres around attitudes towards feedback, highlighting sub-domains of \u003cem\u003efeedback self-efficacy, feedback utility, feedback accountability\u003c/em\u003e, and \u003cem\u003esocial awareness\u003c/em\u003e. While these sub-domains provide a strong foundation for understanding recipient beliefs that might influence engagement with CPF, two key knowledge gaps remained: the relationships between FOS sub-domains (given that most studies treat feedback orientation as a single concept [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]) and the influence of perceived value \u0026ndash; a known antecedent to behavioural intention [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. We then searched the literature related to FOS domains to generate hypotheses for testing.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eFeedback Self-Efficacy and Value as Antecedents of Feedback Accountability\u003c/h2\u003e\n\u003cp\u003eLinderbaum and Levy [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] defined \u003cem\u003efeedback accountability\u003c/em\u003e as an individual\u0026rsquo;s perceived sense of responsibility for acting on feedback, making it a precursor to intention to act on specific feedback information as well as an outstanding driver of the behavioral response itself. Studies that have investigated the subdomains of feedback orientation conceptualized \u003cem\u003efeedback accountability\u003c/em\u003e as a \u003cem\u003ecommitment to action\u003c/em\u003e, which is a more behaviour or action-oriented feature than the subdomains of \u003cem\u003efeedback utility\u003c/em\u003e and \u003cem\u003efeedback self-efficacy\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFeedback self-efficacy\u003c/em\u003e refers to individuals\u0026rsquo; confidence in their ability to act on feedback [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The broader concept of \u003cem\u003eself-efficacy\u003c/em\u003e has been widely investigated in various theories including Social Cognitive Theory [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], the Theory of Planned Behavior [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and Social Cognitive Career Theory [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], with documented positive impact on both intention and behaviour. It has been conceptualized as an individual\u0026rsquo;s cognitive appraisal of their control over achievement and demonstrated as one of the two most important individual antecedents of behaviour from the lens of Control-Value Theory [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the feedback orientation literature, Yang and Yang [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] linked \u003cem\u003efeedback self-efficacy\u003c/em\u003e to \u003cem\u003efeedback accountability\u003c/em\u003e and action (behaviour): individuals who have higher confidence in their ability to deal with feedback are more likely to feel responsible for acting on feedback, and accordingly, act on feedback more proactively.\u003c/p\u003e\n\u003cp\u003eIn addition to \u003cem\u003eself-efficacy\u003c/em\u003e, Control-Value Theory [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] suggests \u003cem\u003evalue\u003c/em\u003e as the second individual antecedent of action. \u003cem\u003eValue\u003c/em\u003e refers to individuals\u0026rsquo; cognitive appraisal of a given action (in the case, the action of interacting with CPF reports) in terms of its personal relevance (i.e., whether the outcome of action matters to individuals). \u003cem\u003eValue\u003c/em\u003e has been demonstrated as an important driver of action in Expectancy-Value Theory [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], the Theory of Planned Behavior [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and Health Belief Model [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eSelf-efficacy\u003c/em\u003e and \u003cem\u003evalue\u003c/em\u003e have been shown to have independent influence on action (i.e., individuals can be best motivated if they have confidence in their ability to act and the corresponding outcome is important to them), highlighting the importance of investigating these two factors simultaneously [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMediating Effect of Feedback Utility\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eFeedback utility\u003c/em\u003e refers to individuals\u0026rsquo; perceived usefulness of feedback in achieving desired outcomes [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Perceived usefulness has been widely investigated in the information system and marketing fields where it operates as a \u0026ldquo;medium attribute\u0026rdquo; (i.e., a characteristic of an information system). This is distinct from the construct of \u003cem\u003evalue\u003c/em\u003e that manifests as an individual/consumer characteristic [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this way, \u003cem\u003evalue\u003c/em\u003e reflects a general belief about what matters to an individual, whereas \u003cem\u003eperceived usefulness\u003c/em\u003e is a context-specific judgment about how well a particular tool or intervention aligns with those values. Prior studies have illustrated the mediating role of the perceived usefulness (\u003cem\u003eutility\u003c/em\u003e) in explaining the effect of \u003cem\u003eself-efficacy\u003c/em\u003e and \u003cem\u003evalue\u003c/em\u003e on intention. Alalwan et al. [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], Wang et al. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] and Youn and Lee [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Alam et al. [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] and Han and Nam [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] further demonstrated the independent positive effects of \u003cem\u003eself-efficacy\u003c/em\u003e and \u003cem\u003evalue\u003c/em\u003e on perceived usefulness as well as the positive effect of perceived usefulness on intention. In the feedback orientation literature, Frondozo and Yang [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] validated the mediating role of \u003cem\u003efeedback utility\u003c/em\u003e in explaining the effect of \u003cem\u003efeedback self-efficacy\u003c/em\u003e on \u003cem\u003efeedback accountability\u003c/em\u003e: individuals who have higher confidence in their ability to deal with feedback tend to perceive feedback as more useful, and as a result, feel higher responsibility for acting on feedback. However, whether the mediating role of \u003cem\u003efeedback utility\u003c/em\u003e also applies to explaining the effect of \u003cem\u003evalue\u003c/em\u003e on \u003cem\u003efeedback accountability\u003c/em\u003e requires further investigation.\u003c/p\u003e\n\u003cp\u003eThese literature supported the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFeedback self-efficacy has a positive relationship with feedback accountability via feedback utility in physicians.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFeedback value has a positive relationship with feedback accountability via feedback utility in physicians.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eChange Discrepancy as an Antecedent of Feedback Value\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eChange discrepancy\u003c/em\u003e refers to individuals\u0026rsquo; awareness of a situation that requires change (perceived need for change), which suggests a sense of relative prioritization and urgency [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Prior studies have demonstrated the crucial role of \u003cem\u003echange discrepancy\u003c/em\u003e as a precursor to initial \u003cem\u003ereadiness for change\u003c/em\u003e and the corresponding change-related \u003cem\u003eactions\u003c/em\u003e (i.e., initiation, persistence, and cooperative behaviours) [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. A high sense of \u003cem\u003echange discrepancy\u003c/em\u003e (derived from both perceiving \u003cem\u003ediscrepancy\u003c/em\u003e and evaluating such \u003cem\u003ediscrepancy\u003c/em\u003e as of high importance) triggers individuals\u0026rsquo; information processing to understand the problem and explore opportunities for change [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Although it lacks empirical evidence in the feedback orientation literature, the logic of this argument stays sound: individuals must perceive the need for change before starting to consider feedback as a candidate solution to initiate behavioural change, developing positive feedback orientation based on cognitive appraisals that lead to a resulting action. As such, we integrate it as an antecedent of the cognitive appraisal of \u003cem\u003evalue\u003c/em\u003e in the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eChange discrepancy has a positive relationship with feedback value in physicians.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTogether, the above hypotheses form the basis of the resulting conceptual model (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003cp\u003eWe used a cross-sectional, multi-method approach to develop a model of physician attitudes and beliefs relating to CPF and to explore variation across key constructs. We achieved this in three sequential steps: (1) validate the model of engagement with CPF; (2) quantitatively explore recipient heterogeneity across key constructs; and (3) qualitatively explore recipient heterogeneity across key constructs. The protocol was approved by the Trillium Health Partners Research Ethics Board (ID # 1073). All participants provided informed consent prior to survey completion and interviews.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eContext and Setting\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in the primary care setting. In Ontario, Canada\u0026rsquo;s most populous province, most of the population (83%) has a primary care physician (PCP) as their first point of contact within the healthcare system [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], with approximately 14,500 physicians providing primary care services as of 2021 [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. PCPs may work in various practice settings, such as solo practices, group practices, or Community Health Centres, and have different remuneration schedules available to them, ranging from fee-for-service to capitation to salary-based. Most PCPs in Ontario are paid through a blended capitation model, where they receive a fixed amount of money per patient registered to their practice based on factors such as age, sex, and health status (similar to capitation) as well as additional payments for specific services performed (similar to fee-for-service) [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSince 2013, all physicians in Ontario who voluntarily register received a CPF report from a government agency responsible for connecting and improving health care in the province. The report provides information about their practice, including prescribing and screening rates across a variety of clinical topics. Individual trends in each performance indicator are compared to peers and \u0026ldquo;change ideas\u0026rdquo; are included with links to educational resources and practice-based tools to support quality improvement. The CPF report is confidential and is not used for performance management. In 2017, four opioid prescribing indicators pertaining to non-palliative care patients were added to the report. A full mock report from 2020 is included in Additional File 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStep 1: Model Validation\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipant Recruitment\u003c/h2\u003e\n\u003cp\u003eEligible participants included all registered PCPs (i.e., specialty of family medicine or general practice) in Ontario, Canada (n \u0026cong; 14,700 as of 2018) [ \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e ]. Recruitment occurred via an online survey distributed via three channels: (1) the mailing lists of key organizations in primary care, (2) a targeted email from Ontario Health, the agency that oversees healthcare administration in the province of Ontario, to registered recipients of their MyPractice: Primary Care reports, and (3) social media channels (i.e., X). All recruitment materials provided a direct link to a letter of information and a subsequent electronic survey for those who consented. Survey participants were given the option to receive a $50 gift card for completing the survey.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003eThe electronic survey included constructs from two validated and widely adopted scales: the Feedback Orientation Scale (FOS) and the Organizational Change Recipients\u0026rsquo; Beliefs Scale (OCRBS). The FOS [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] measured feedback accountability, feedback self-efficacy, and feedback utility. We omitted the social awareness construct as CPF does not provide a means for recipients to understand how others view them. The OCRBS [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] measured perceptions on feedback value and change discrepancy. All items were measured on a 5-point Likert scale ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree\u0026rdquo;. To better align with the context of survey participants, we modified the original OCRBS items to contextualize statements around the need for change (i.e., \u003cem\u003echange discrepancy\u003c/em\u003e) and perceived intrinsic and altruistic value that would come with receiving feedback (i.e., \u003cem\u003efeedback value\u003c/em\u003e) in the healthcare field. An overview of constructs and definitions can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u0026ndash; Survey constructs measuring recipient characteristics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConstruct of interest\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource Survey\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOperational Definition\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFeedback utility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe belief that CPF will improve the desired outcomes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFeedback self-efficacy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConfidence in the ability to act on CPF\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccountability\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe sense of responsibility for acting on CPF\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange discrepancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOCRBS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe belief that something needs to improve\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOCRBS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhether CPF provides value in line with the perceived discrepancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eCPF\u0026thinsp;=\u0026thinsp;Clinical perfromance feedback; FOS\u0026thinsp;=\u0026thinsp;Feedback Oreintation Scale; OCRBS\u0026thinsp;=\u0026thinsp;Organizational Change Recipients\u0026rsquo; Beliefs Scale.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eCommon method bias was assessed via multi-collinearity [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] and goodness-of-fit [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e] and Confirmatory Factor Analysis was conducted to test the discriminant validity of the study constructs [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Partial Least Squares Path Modeling (PLS-PM) was then applied to test the conceptual model due to its capability of investigating complex latent construct models using a small sample size with non-parametric data [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. The PLS-PM model was estimated to measure the direct effects, indirect effects, and total effects of the included constructs. Missing values were replaced by the mean value. To test the statistical significance of the PLS-PM estimates and provide the corresponding confidence intervals, a bootstrapping procedure based on 5000 resamples was applied [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. Mediation analysis followed Zhao, Lynch and Chen [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. All statistical analyses were performed in R 4.4.1, using packages including tidyLPA, lavaan, nnet, SEMinR, and plspm.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter data cleaning the survey had 206 responses (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for participant demographics). Results of all construct level Variance Inflation Factor scores were under the threshold of 3, indicating absence of multi-collinearity [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. Goodness-of-fit was 0.49, higher than the cut-off of 0.36 to be considered as globally fit [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. The original model yielded a satisfactory model fit to the sample data [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e], including: Comparative Fit Index (CFI)\u0026thinsp;\u0026gt;\u0026thinsp;0.90, Tucker\u0026ndash;Lewis Index (TLI)\u0026thinsp;\u0026gt;\u0026thinsp;0.90, Root Mean Square Error of Approximation (RMSEA)\u0026thinsp;\u0026lt;\u0026thinsp;0.08, Standardized Root Mean Squared Residual (SRMR)\u0026thinsp;\u0026lt;\u0026thinsp;0.08, and scaled 𝛘2/degree of freedom\u0026thinsp;\u0026lt;\u0026thinsp;3 (see Additional File 2). Moreover, the original model had better model fit than the reduced models in all criteria. These analysis results indicated that the Common Method Bias was not an issue. After removing two items that did not meet the indicator loading threshold of 0.6 [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e], the model demonstrated acceptable internal consistency, construct validity [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e], and discriminant validity [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] (refer to Additional File 3 for detailed results).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipant Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 3 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;4 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily Health Organization (FHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmaller areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff number within the organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the integrated model and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents means, standard deviations, and inter-correlations of the study constructs.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeans, Standard Deviations, and Inter-Correlations of Study Constructs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange discrepancy (C1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeedback value (C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeedback self-efficacy (C3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeedback utility (C4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeedback accountability (C5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFeedback self-efficacy has a positive relationship with feedback accountability via feedback utility.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows, we observed a significantly positive association between \u003cem\u003efeedback self-efficacy\u003c/em\u003e and \u003cem\u003efeedback utility\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, 95% CI: 0.21, 0.44), between \u003cem\u003efeedback utility\u003c/em\u003e and \u003cem\u003efeedback accountability\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.38, 0.65), but no significant association between \u003cem\u003efeedback self-efficacy\u003c/em\u003e and \u003cem\u003efeedback accountability\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, 95% CI: -0.11, 0.16). Taking into consideration the significant indirect effect of \u003cem\u003efeedback self-efficacy\u003c/em\u003e on \u003cem\u003efeedback accountability\u003c/em\u003e via \u003cem\u003efeedback utility\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, 95% CI: 0.10, 0.25), the mediation analysis revealed a full mediation effect of \u003cem\u003efeedback utility\u003c/em\u003e, supporting Hypothesis \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFeedback value has a positive relationship with feedback accountability via feedback utility.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe observed a statistically significant positive association between \u003cem\u003efeedback value\u003c/em\u003e and \u003cem\u003efeedback utility\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.40, 0.61) and between \u003cem\u003efeedback utility\u003c/em\u003e and \u003cem\u003efeedback accountability\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.38, 0.65). The mediation analysis yielded a significant indirect effect of \u003cem\u003efeedback value\u003c/em\u003e on \u003cem\u003efeedback accountability\u003c/em\u003e via \u003cem\u003efeedback utility\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, 95% CI: 0.17, 0.36). Considering a significant direct effect of \u003cem\u003efeedback value\u003c/em\u003e on \u003cem\u003efeedback accountability\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.09, 0.35), the result indicated a complementary mediation effect of \u003cem\u003efeedback utility\u003c/em\u003e, supporting Hypothesis \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eChange discrepancy has a positive relationship with feedback value.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe observed a statistically significant positive relationship between \u003cem\u003echange discrepancy\u003c/em\u003e and \u003cem\u003efeedback value\u003c/em\u003e (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48, 95% CI: 0.36, 0.59), supporting Hypothesis \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eStep 2: Quantitatively explore recipient heterogeneity\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003eParticipant Recruitment and Data Collection\u003c/h2\u003e\n \u003cp\u003eThe same participants and electronic survey data described in Step 1 were used.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eWe conducted a Latent Profile Analysis (LPA), a novel person-centered approach [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] not yet applied in the context of CPF, to identify the unique participant profiles that contribute to population heterogeneity in feedback orientation (i.e., \u003cem\u003efeedback self-efficacy\u003c/em\u003e, \u003cem\u003efeedback utility\u003c/em\u003e, and \u003cem\u003efeedback accountability\u003c/em\u003e). Confirmatory factor analysis (CFA) was used to validate the assumptions that the identified latent models were distinct and reliable [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. To address non-normality and missing values, we used Maximum Likelihood with Robust standard errors estimation and Full Information Maximum Likelihood estimation, respectively. We extracted the factor scores of each construct following Scherer et al. [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] to generate more accurate estimates for each latent construct.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eAfter data cleaning the survey had 206 responses (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with the majority being woman (61.2%), Canadian trained physicians (81.4%), with more than 5 years of practice experience (69.9%). Participants reported working more than 3 days/week (62.6%), with more than 5 colleagues (53.9%). Nearly half of the participants (45.6%) were based in an urban centre (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for survey participant demographics).\u003c/p\u003e\n\u003cp\u003eLPA identified three distinct class profiles (see Additional File 4), assigning 32 participants to Class 1, 143 participants to Class 2, and 31 participants to Class 3. Participants in Class 1 exhibited consistently higher factor scores than 0 (i.e., overall sample mean) on all profile indicators; those in Class 2 exhibited factor scores around 0 on all profile indicators; whereas those in Class 3 exhibited factor scores lower than 0, especially for \u003cem\u003efeedback self-efficacy\u003c/em\u003e (see Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In this case, we labelled these three classes as \u0026ldquo;high and balanced feedback orientation\u0026rdquo;, \u0026ldquo;moderate and balanced feedback orientation\u0026rdquo;, and \u0026ldquo;low feedback orientation (especially \u003cem\u003efeedback self-efficacy\u003c/em\u003e)\u0026rdquo;, respectively.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reports the association of demographic and psychological characteristics with feedback orientation class membership. Using Class 2 as a reference group, we found that \u003cem\u003efeedback value\u003c/em\u003e and \u003cem\u003echange discrepancy\u003c/em\u003e were positively associated with the likelihood of being assigned to Class 1, e.g., for every one-unit increase in \u003cem\u003efeedback value\u003c/em\u003e, the odds of being assigned to Class 1 increased by 508% compared to being assigned to Class 2. \u003cem\u003eFeedback value\u003c/em\u003e was negatively associated with the likelihood of being assigned to Class 3 compared to being assigned to Class 2. Additionally, women were more likely to be assigned to Class 3, whereas those who worked in a practice model other than FHO were less likely to be assigned to Class 3 as compared to being assigned to Class 2.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and Psychological Characteristics Predicting Profile Membership of Feedback Orientation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eClass 1 vs Class 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eClass 3 vs Class 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeedback value (OCRBS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11,\u0026nbsp;17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12,\u0026nbsp;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange discrepancy (OCRBS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30,\u0026nbsp;11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77,\u0026nbsp;3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Woman vs Man)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18,\u0026nbsp;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56,\u0026nbsp;16.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational graduate (Yes vs No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19,\u0026nbsp;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46,\u0026nbsp;5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice year (\u0026ge;\u0026thinsp;6 vs\u0026thinsp;\u0026le;\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37,\u0026nbsp;3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59,\u0026nbsp;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork days/week (\u0026ge;\u0026thinsp;4 vs\u0026thinsp;\u0026le;\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39,\u0026nbsp;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37,\u0026nbsp;2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice model (Other vs FHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26,\u0026nbsp;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12,\u0026nbsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity size (Smaller size vs urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22,\u0026nbsp;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34,\u0026nbsp;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff number (\u0026ge;\u0026thinsp;6 vs\u0026thinsp;\u0026le;\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14,\u0026nbsp;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38,\u0026nbsp;2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eN.B. Class 1: high and balanced feedback orientation, Class 2: moderate and balanced feedback orientation, Class 3: low feedback orientation (especially \u003cem\u003eself-efficacy\u003c/em\u003e).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eStep 3: Qualitatively explore recipient heterogeneity\u003c/h2\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003eParticipant Recruitment\u003c/h2\u003e\n \u003cp\u003eWe used a convenience sampling approach for the interviews by including a question at the end of the electronic survey asking participants if they were interested in participating in a follow-up interview to discuss their beliefs and perspectives relating to CPF. Participants were advised they would be provided with a $150 honorarium for interview participation. We aimed to recruit PCPs eligible to receive the CPF report, whether they signed up for it or not. We contacted interested participants in consecutive order (i.e., starting with those that completed the survey first). We continued recruitment until no new insights emerged from the interviews.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eData Collection\u003c/h2\u003e\n \u003cp\u003eA convenience sampling approach operationalized through the online survey generate a list of eligible participants. Semi-structured, exploratory interviews were informed by the survey constructs (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and sought to understand participant perspectives on CPF (see Additional File 5 for interview guide). Questions explored perspectives and beliefs relating to performance improvement in primary care, participant goals around quality of care, recipient and contextual factors perceived to influence engagement with CPF, and experiences with CPF. Example questions included \u0026lsquo;What role does feedback play for primary care physicians and the care they provide\u0026rsquo; and \u0026lsquo;How does feedback contribute to your success (or not) as a primary care physician?\u0026rsquo;. As the focus of this study was on recipient attitudes and characteristics, data on contextual factors will be reported separately. Interviews were conducted virtually and were audio-recorded and transcribed verbatim by an independent third party.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eTranscripts were coded using MAXQDA. Interviews were analyzed using the framework method [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e], with survey constructs and CP-FIT domains applied as pre-defined deductive codes. Inductive coding was applied when concepts were identified that did not fit within the definitions of pre-defined constructs. This approach supported exploration of potential interactions between individual attitudes and beliefs that may influence engagement with CPF. Once descriptive statements were generated to detail how each construct manifested in the data, we reviewed the transcripts to identify how these characteristics intersected with one another to generate themes. To support the framework analysis, qualitative findings on recipient factors influencing engagement were triangulated with participant survey results to achieve a deeper understanding of how these factors coalesce to shape the perceptions and beliefs of physicians [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. This triangulation helped identify the characteristics (i.e., distinct attitudes and beliefs) that varied between individuals, enabling the development of typologies for participants who recognized the value of CPF and those who did not. These insights support the refinement of the conceptual model.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eNine interviews were conducted with PCPs, ranging from 40 minutes to 62 minutes in duration. Additional File 6 outlines participant demographics and survey scores. Two distinct viewpoints emerged \u0026ndash; one where participants did not see value in CPF, emphasizing its limitations and undermining perceived accountability (hereafter referred to as non-engagers), and one where participants acknowledged the limitations of CPF but still believed in its value and their accountability to improve (hereafter referred to as engagers). Simply put, some participants justified their limited use of the report \u003cem\u003ebecause of\u003c/em\u003e its limitations (n\u0026thinsp;=\u0026thinsp;5) while others perceived it as useful \u003cem\u003edespite\u003c/em\u003e its limitations (n\u0026thinsp;=\u0026thinsp;4). Four key perceptions distinguished these groups, which are described below in line with the constructs described above.\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived need for change and value of CPF\u003c/h2\u003e\n \u003cp\u003ePhysicians unanimously recognized the need for change within the primary care landscape, citing administrative burdens, deferred care due to the pandemic, and the departure of primary care physicians from the profession as factors that drive physician burnout (and therefore necessitate change). Despite a shared acknowledgement of the need for change, participants held varying perspectives on the role of CPF in informing change. Engagers tended to view CPF as a catalyst, actively considering themselves among those responsible to drive some of these changes. Conversely, non-engagers downplayed the role of CPF, emphasizing changes outside their professional scope as more impactful.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;It\u0026rsquo;s like, patients are already getting reminders from [a regional support organization] for all three of these, breast, colon, cervical. Patients can book their own mammograms. Ideally, if I was going to change anything, why doesn\u0026apos;t the province mail colon cancer screening kits directly to patients, instead of me having to tell the labs to do it? Some of these things are like, you\u0026rsquo;re telling me these numbers, so what? So I can call the patients and remind them they got a letter in the mail? Is that what you want me to do? Because you already have [the regional organization] doing this stuff, and now you\u0026rsquo;re asking me to do it as well.\u0026rdquo; P6, Non-Engager\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThese varying perspectives aligned with participant scores on the OCRBS. While engagers and non-engagers scored similarly on the \u0026lsquo;Discrepancy\u0026rsquo; factor, which measures the extent to which one feels that there are legitimate needs for change, non-engagers scored lower on the \u0026lsquo;Valence\u0026rsquo; factor, which measures the extent to which one believes that they will benefit from engagement with CPF. Importantly, non-engagers stressed that their criticism of CPF was not directed at the general practice of collecting data to inform practice, but rather at its implementation within currently available reports providing feedback at the practice level.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived utility of CPF for addressing practice-related gaps\u003c/h2\u003e\n \u003cp\u003eEngagers perceived the report was helpful in supporting them in their role, including general improvement and identifying practice-related gaps and setting and monitoring goals aimed at addressing them. One physician explained:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;There\u0026rsquo;s this quality improvement project we have to do every five years, so I believe that might have been part of the impetus and [\u0026hellip;] use this as one avenue in which to explore how we\u0026rsquo;re going to improve from a quality improvement lens.\u0026rdquo; P10 (Engager)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eNon-engagers were less likely to perceive this benefit, which was supported by their lower scores on the \u0026lsquo;Utility\u0026rsquo; domain of the FOS. Many non-engagers expressed having alternative ways of identifying practice-related gaps, such as leveraging other sources of data-driven feedback (e.g., system level reports that identified specific patients that need action) or querying their electronic medical record (EMR) system. In fact, most non-engagers claimed that physicians should already have a sense of their performance relative to provincial averages, leading them to believe the report didn\u0026rsquo;t provide any novel insight.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;I think [the CPF report] said I had more than the average opioid patients. I could have told them that. I run searches on my patients. We\u0026rsquo;ve done audits to look for people who are on benzodiazepines, or higher doses of opioids. I have a list that I go through every few months. So, it\u0026rsquo;s less helpful for me personally because I\u0026rsquo;m already on top of the things that I can track and change.\u0026rdquo; P1, Non-Engager\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAdditionally, some non-engagers believed that CPF reports were primarily targeting low performers\u0026mdash;a belief that stemmed from perceptions regarding the goal of CPF reports. Non-engagers perceived the reports as a way of standardizing practice patterns, increasing adherence to clinical guidelines, or reducing healthcare costs. These goals were viewed as distinct from facilitating practice improvement and conflicted with the way some physicians viewed their role.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;I was thinking to myself, why would [a regional organization] generate these reports? What\u0026rsquo;s it for? And ultimately, I\u0026rsquo;m like, \u0026lsquo;Oh, it\u0026rsquo;s because they want to save healthcare dollars.\u0026rsquo; Which is, ultimately, a good thing, right? [\u0026hellip;] But the guidelines from [the regional organization] are based on populations. When I practice, I\u0026rsquo;m only thinking of the patient in front of me. I\u0026rsquo;m rarely thinking of the population. So, if you say to someone, \u0026ldquo;I\u0026rsquo;m recommending a FIT test, but actually a colonoscopy is better. We just can\u0026rsquo;t afford to do it for everybody.\u0026rdquo; They\u0026rsquo;re going to want the colonoscopy, right? They don\u0026rsquo;t care that [FIT tests are] more economical for Ontario at a population level, and nor should I, to some degree.\u0026rdquo; P2, Non-Engager\u003c/em\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived utility influences sense of accountability for engaging with and acting on CPF\u003c/h2\u003e\n \u003cp\u003eEngagers and non-engagers held varying perceptions regarding the level of accountability they feel for engaging with and acting upon CPF, which aligned with scores on the \u0026lsquo;Accountability\u0026rsquo; domain of the FOS. Engagers often described accountability as linked to comprehensive care.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eThere\u0026rsquo;s very little time, and there\u0026rsquo;s a lot of administrative duties. But part of my responsibility is to make sure I\u0026rsquo;m delivering high-quality care and trying to improve. So, I wouldn\u0026apos;t say it\u0026rsquo;s extra admin stuff that I don\u0026rsquo;t have time for. I mean, to a degree, we have to make time. That\u0026rsquo;s part of the comprehensive care we\u0026apos;re giving.\u0026rdquo; P9, Engager\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eTwo key factors emerged as shaping participants\u0026rsquo; perceptions of accountability: the actionability of the feedback within the report and the broader organizational setting in which they worked.\u003c/p\u003e\n \u003cp\u003eIn terms of actionability, participants unanimously viewed CPF related to preventative cancer screening and hemoglobin A1C testing as more actionable compared to feedback related to opioid and antibiotic prescribing. Physicians viewed outside providers, such as pain clinics and walk-in clinics, as confounding opioid and antibiotic prescribing numbers, reducing participants\u0026rsquo; sense of control and causing some to dismiss the data. This issue is compounded by the fact that practice-level CPF reports do not identify which patients have been prescribed these drugs and, unlike for preventative cancer screening, such information cannot be readily queried.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;If I\u0026apos;m not able to tease out who it\u0026rsquo;s reflective of, in terms of actual patients, then it\u0026rsquo;s not useful. Because I actually don\u0026rsquo;t know. Let\u0026rsquo;s say, for example, it came back with something astronomically high, like 80 percent of your patients are prescribed opioids. OK, what\u0026rsquo;s actionable based on that? I guess I can ask patients more often if they\u0026apos;re being prescribed opioids by other providers, but I can\u0026apos;t affect other providers\u0026rsquo; prescribing habits.\u0026rdquo; P6, Non-Engager\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eEngagers were still more likely to find these indicators valuable, taking further lengths to contextualize and reflect on the data in ways that non-engagers did not. For example, one participant considered whether certain demographic characteristics of their patient population, such as rurality and proportion of patients with labor-intensive jobs, could be an influencing factor in the high prescribing rates of opioids. Another participant considered making changes to their practice to encourage patients to discuss opioid use, potentially paving the way for adjustments to treatment plans and deprescribing. Another described reflecting on their practice patterns in the context of broader patient outcomes:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;The section on antibiotics, which is a new section, what was my antibiotic initiation rate [\u0026hellip;]\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIn March was below average. And then I went above average again. So the number of antibiotic\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003etreatment episodes prescribed by me in the last six months was 16. [\u0026hellip;] They\u0026rsquo;re basically saying\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003efewer antibiotics is better, but I would say the right number of antibiotics is better. It would be\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003einteresting to see how your hospitalization rate compares to your antibiotic prescribing rate.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003eBecause perhaps your antibiotics are preventing hospitalizations.\u0026rdquo; P15 (Engager)\u003c/h2\u003e\n \u003cp\u003eSimply put, engagers acknowledged the importance of contextualizing the report\u0026rsquo;s findings to derive insights while non-engagers wanted insights to be self-evident and readily actionable.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Findings\u003c/h2\u003e \u003cp\u003eThe results of this multi-method study provide insight into the factors that influence physician engagement with CPF and how they interact. Specifically, perceived change discrepancy (whether physicians think that something needs to improve), value (whether CPF provides value in line with the perceived discrepancy), self-efficacy (the physician\u0026rsquo;s confidence in their ability to act on CPF), utility (whether physicians believe CPF will improve the desired outcomes), and accountability (sense of responsibility for acting on CPF) influence engagement with CPF. These insights advance our understanding of what to target to address commonly cited engagement challenges with CPF [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], and enhances CPF theory (specifically CP-FIT) by providing complementary insights on which recipient characteristics engagement with CPF and how they interact. However, the way these constructs manifest varies across physicians, with individual differences shaping the degree to which CPF is perceived as beneficial or actionable. These results extend prior work by demonstrating that engagement is not automatic, but rather is influenced by physicians' interpretation of its relevance, usability, and their own capacity to act on it \u0026ndash; and that these interpretations themselves vary across physicians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Existing Literature\u003c/h2\u003e \u003cp\u003eThe perceived value of CPF hinges on the belief that things need to change and its relevance to daily practice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our findings expand on best practice guidance for CPF [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and build on prior work that has found physicians do not engage with CPF when they perceive no benefit [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] by specifying the factors that influence how physician\u0026rsquo;s assess benefit. Physicians must first recognize a need for change (\u003cem\u003echange discrepancy\u003c/em\u003e) and view CPF as providing relevant, useful data. When these conditions are met, engagement is more likely. However, our analysis captures a single point in time, and further research is needed to understand how these perceptions evolve and what strategies effectively shift them. Our study extends beyond the focus on barriers to engagement with CPF to highlight actionable pathways to foster engagement by specifying high-value determinant to target.\u003c/p\u003e \u003cp\u003ePhysician self-assessment is often inaccurate [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], limiting opportunities for professional growth and emphasizing the need for external assessments to guide improvement. CPF provides a scalable way to help physicians identify habitual processes in their practice patterns. However, engagement is not uniform; physicians vary in their confidence (self-efficacy) and willingness to act on CPF, influenced by their past experiences, learning preferences, and perceived relevance of the data. Many physicians lack confidence (self-efficacy) in using such data effectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], aligning with the results of our latent profile analysis which showcase the role of self-efficacy in shaping feedback orientation. To effectively support improvement, CPF initiatives must simultaneously mitigate concerns and increase confidence in acting on the data, helping physicians to interpret population-level data and apply it to individual patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Kluger and Nir\u0026rsquo;s feedforward approach [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] may help by shifting the focus from critiquing past performance to guiding future actions, highlighting actionable next steps. This approach aligns with the NCF, emphasizing CPF\u0026rsquo;s benefits (e.g., growth and improvement) while reducing perceived judgment (concerns). By linking self-efficacy to engagement with CPF, our findings reinforce the importance of designing CPF strategies that explicitly build physician confidence and competence in data use, rather than assuming these exist at baseline.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImplications for Practice\u003c/h3\u003e\n\u003cp\u003eCPF initiatives should be designed with key engagement factors in mind, and implementation efforts should proactively communicate how CPF meets these needs. For example, specifying how the data aligns to physician values, including aspirations relating to clinical care, communication goals, and protecting time with patients downstream [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], may increase perceived benefit. Those designing CPF should consider the explicit cues that would encourage a new course of action (i.e., engagement with CPF or a practice change). Situational strength\u0026mdash;the contextual cues that shape behavior\u0026mdash;plays a key role in influencing whether physicians change course [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. In the context of CPF, high clarity provides explicit guidance on actions that improve outcomes, while high consequences reinforce the impact of these actions on patient care.\u003c/p\u003e \u003cp\u003eDelivering CPF with future-oriented goals and actionable guidance can make it more constructive, reframing it as a learning opportunity rather than a critique. Combining this approach with facilitated coaching conversations can help physicians create concrete plans to act on CPF, closing the oft-cited gap between intention and action [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. These conversations should cue physicians to identify potential changes and guide them to making those changes routine practice, ultimately enabling them to intuitively respond to CPF without relying on effortful interpretation amid competing demands [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Taken together, these strategies provide a structured and evidence-informed roadmap for improving CPF engagement to better position it for impact on clinical practice.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003ePrimary care physicians in Ontario, Canada receive numerous CPF reports from various sources, therefore it is possible that preconceived notions of what feedback is could have affected the measurement of the related constructs. Future work should explore existing mental models around feedback in a healthcare context to identify whether and how a new model of feedback is needed in healthcare. Additional work is needed to explore whether the findings in this study extend beyond the current sample (primary care physicians), including other medical specialties and clinicians who receive CPF. Given the widespread and increasing frequency with which data is provided to physicians, future work should explore whether and how these beliefs may systematically affect physicians\u0026rsquo; judgements and decisions as it relates to using data more generally to identify and drive improvements in their practice should be explored in future work. The results from this study can be used as the basis for interventional studies, including those that engage physicians in co-designing CPF, to support better specification of how to address value, utility, confidence, and accountability in practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study offers novel insights into the cognitive appraisal process that influences whether physicians voluntarily engage with CPF. The evidence-based design of CPF has the potential to support physician development with an emphasis on improving patient outcomes. Specification of the factors informing CPF appraisal support the personalization of performance feedback strategies \u0026ndash; akin to personalizing care for the patient in front of you. Based on our findings, we propose that CPF initiatives explicitly state how CPF supports better patient outcomes, maintain a future-focus, and provide clear instructions about the behaviour(s) leading to improved outcomes. Recognizing that \u0026lsquo;intention is everything\u0026rsquo; [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], CPF initiatives must clearly communicate how they support professional growth and improve patient outcomes in order to realize their promise of supporting health system improvement at scale.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Performance Feedback\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCP-FIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Performance Feedback Intervention Theory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFeedback Oreintation Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNecessity-Concerns Framework\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCRBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrganizational Change Recipients\u0026rsquo; Beliefs Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary care physician\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Root Mean Squared Residual\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTucker\u0026ndash;Lewis Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Trillium Health Partners Research Ethics Board (Protocol ID #1073). All participants provided informed consent prior to participation in the survey and interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain any individual person\u0026rsquo;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to participant confidentiality, but may be available from the corresponding author on reasonable request and with appropriate ethical approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by funding from the Canadian Institutes of Health Research (PJT 178046). The funder had no role in the design of the study, data collection, analysis, interpretation of data, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLD conceived of the study and led the design, analysis, and manuscript preparation. RW led the quantitative analyses and supported model development, with the support of SCM. BT led the qualitative data collection and analyses with the support of LD. NMI, BB, AH, FR, AV, GR, and MT contributed to data interpretation and manuscript revisions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the physicians who generously shared their time and perspectives, and the team at Ontario Health for supporting survey distribution. We also acknowledge the support of research staff at the Institute for Better Health for project coordination and data management.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarrison R, Hinchcliff RA, Manias E, Mears S, Heslop D, Walton V, et al. Can feedback approaches reduce unwarranted clinical variation? A systematic rapid evidence synthesis. BMC Health Serv Res. 2020;20(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database of Systematic Reviews. 2012; 2012(7):CD000259-CD59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCleary N, Desveaux L, Presseau J, Reis C, Witteman HO, Taljaard M, et al. Engagement is a necessary condition to test audit and feedback design features: results of a pragmatic, factorial, cluster-randomized trial with an embedded process evaluation. Implement Sci. 2023;18(1):13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvers NM, Taljaard M, Giannakeas V, Reis C, Mulhall CL, Lam JMC, et al. Effectiveness of confidential reports to physicians on their prescribing of antipsychotic medications in nursing homes. Implement Sci Commun. 2020;1(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, et al. Practice feedback interventions: 15 suggestions for optimizing effectiveness. Ann Intern Med. 2016;164(6):435\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Connor DA, Glasziou P, Maher CG, McCaffery KJ, Schram D, Maguire B, et al. Effect of an individualized audit and feedback intervention on rates of musculoskeletal diagnostic imaging requests by Australian general practitioners: a randomized clinical trial. JAMA. 2022;328(9):850\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown B, Gude WT, Blakeman T, van der Veer SN, Ivers N, Francis JJ, et al. Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci. 2019;14(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrent SA, Havranek EP, Ginde AA, Haukoos JS. Effect of audit and feedback on physician adherence to clinical practice guidelines for pneumonia and sepsis. Am J Med Qual. 2019;34(3):217\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrietsch J, van Steenkiste B, Grol R, Winkens B, Ulenkate H, Metsemakers J, et al. Effect of audit and feedback with peer review on general practitioners' prescribing and test ordering performance: a cluster-randomized controlled trial. BMC Fam Pract. 2017;18(1):53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Burlacu G, Truxillo D, James K, Yao X. Age differences in feedback reactions: the roles of employee feedback orientation on social awareness and utility. J Appl Psychol. 2015;100(4):1296\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraddy PW, Sturm RE, Atwater LE, Smither JW, Fleenor JW. Validating the Feedback Orientation Scale in a leadership development context. Group Organ Manage. 2013;38(6):690\u0026ndash;716.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesveaux L, Ivers NM, Devotta K, Ramji N, Weyman K, Kiran T. Unpacking the intention to action gap: a qualitative study understanding how physicians engage with audit and feedback. Implement Sci. 2021;16(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips LA, Diefenbach MA, Kronish IM, Negron RM, Horowitz CR. The Necessity-Concerns Framework: a multidimensional theory benefits from multidimensional analysis. Ann Behav Med. 2014;48(1):7\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeloski J, Boex JR, Grasberger MJ, Evans A, Wolfson DB. Systematic review of the literature on assessment, feedback and physicians' clinical performance: BEME Guide 7. Med Teach. 2006;28(2):117\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSargeant J, Bruce D, Campbell CM. Practicing physicians' needs for assessment and feedback as part of professional development. J Continuing Educ Health Professions. 2013;33:S54\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerguson J, Wakeling J, Bowie P. Factors influencing the effectiveness of multisource feedback in improving the professional practice of medical doctors: a systematic review. BMC Med Educ. 2014;14:76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesveaux L, Nguyen MD, Ivers NM, Devotta K, Upshaw T, Ramji N, et al. Snakes and ladders: a qualitative study understanding the active ingredients of social interaction around the use of audit and feedback. Translational Behav Med. 2023;13(5):316\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvers N, Barnsley J, Upshur R, Tu K, Shah B, Grimshaw J, et al. My approach to this job is \u0026hellip; one person at a time Perceived discordance between population-level quality targets and patient-centred care. Canadian Family Physician. 2014; 60(3):258\u0026thinsp;\u0026ndash;\u0026thinsp;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouleau G, Reis C, Ivers N, Desveaux L. Characterizing the gaps between best-practice implementation strategies and real-world implementation: qualitative study among family physicians who engaged with audit and feedback reports. JMIR Hum Factors. 2023;10:e38736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinderbaum BA, Levy PE. The development and validation of the Feedback Orientation Scale (FOS). J Manag. 2010;36(6):1372\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDahling JJ, Chau SL, O'Malley A. Correlates and consequences of feedback orientation in organizations. J Manag. 2012;38(2):531\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmither JW, London M, Reilly RR. Does performance improve following multisource feedback? A theoretical model, meta-analysis, and review of empirical findings. Pers Psychol. 2005;58(1):33\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePekrun R. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational Psychol Rev. 2006;18(4):315\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process. 1991;50(2):179\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang M. Exploring the power of teacher feedback in Chinese students: testing the relationships between students\u0026rsquo; feedback beliefs and student engagement. In: Yang L, Yang M, editors. Asian Education Miracles. London: Routledge; 2019. pp. 155\u0026thinsp;\u0026ndash;\u0026thinsp;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrondozo CE, Yang L. Changing external feedback to learning opportunities: a study on Filipino university students' feedback orientation. In: Yan Z, Yang L, editors. Assessment as Learning:Maximising Opportunities for Student Learning and Achievement. New York, NY: Routledge; 2022. pp. 190\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandura A. Social cognitive theory: an agentic perspective. Ann Rev Psychol. 2001;52:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLent RW, Brown SD. Social Cognitive Career Theory at 25: empirical status of the interest, choice, and performance models. J Vocat Behav. 2019;115:103316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo WS, Ng PT, Lee K, Aye KM. Self-efficacy, value, and achievement emotions as mediators between parenting practice and homework behavior: a control-value theory perspective. Learn Individual Differences. 2016;50:275\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoick J, Ringeisen T. Self-efficacy, test anxiety, and academic success: A longitudinal validation. Int J Educational Res. 2017;83:84\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEccles JS, Adler TF, Futterman R, Goff SB, Kaczala CM, Meece JL, et al. Expectancies, values and academic behaviors. In: Spence JT, editor. Achievement and achievement motives. San Francisco, CA: W. H. Freeman; 1983. pp. 75\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenstock IM. Historical origins of health belief model. Health Educ Monogr. 1974;2(4):328\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyalew M, Zewdie S. What factors determine the online consumer behavior in this digitalized world? A systematic literature. Human Behavior and Emerging Technologies. 2022; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlalwan AA, Dwivedi YK, Rana NP, Williams MD. Consumer adoption of mobile banking in Jordan examining the role of usefulness, ease of use, perceived risk and self-efficacy. J Enterp Inform Manage. 2016;29(1):118\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Chou S-cT, Chang H-C. Examining the impacts of perceived value and perceived quality on users' intention to join web 2.0 communities. In: Proceedings of the 11th International Conference on Electronic Commerce. Taipei, Taiwan: Association for Computing Machinery; 2009. pp. 329\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoun S-y, Lee K-H. Proposing value-based technology acceptance model: testing on paid mobile media service. Fashion Textiles. 2019;6(1):13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam SS, Masukujjaman M, Ahmad M, Jaffor R. Acceptance of online distance learning (ODL) among students: mediating role of utilitarian and hedonic value. Educ Inform Technol. 2023;28(7):8503\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan S, Nam SI. Creating supportive environments and enhancing personal perception to bridge the digital divide among older adults. Educ Gerontol. 2021;47(8):339\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmenakis AA, Bernerth JB, Pitts JP, Walker HJ. Organizational Change Recipients' Beliefs Scale: development of an assessment instrument. J Appl Behav Sci. 2007;43(4):481\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiner BJ. A theory of organizational readiness for change. Implement Sci. 2009;4:67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolt DT, Helfrich CD, Hall CG, Weiner BJ. Are you ready? How health professionals can comprehensively conceptualize readiness for change. J Gen Intern Med. 2010;25:50\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafferty AE, Minbashian A. Cognitive beliefs and positive emotions about change: Relationships with employee change readiness and change-supportive behaviors. Hum Relat. 2019;72(10):1623\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge JM, Jones GR. Towards a process model of individual change in organizations. Hum Relat. 2001;54(4):419\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistics Canada. 2020; Primary health care providers, 2019 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www150.statcan.gc.ca/n1/pub/82-625-x/2020001/article/00004-eng.htm]\u003c/span\u003e\u003cspan address=\"http://www150.statcan.gc.ca/n1/pub/82-625-x/2020001/article/00004-eng.htm]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed March 24 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiran T, Green ME, Wu CFY, Kopp A, Latifovic L, Frymire E, et al. Family physicians stopping practice during the COVID-19 pandemic in Ontario, Canada. Ann Fam Med. 2022;20(5):460\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward M, Wu F, Kiran T, Frymire E, Lapointe-Shaw L, Jaakkimainen L, et al. The impact of the COVID 19 pandemic on continuity of care with Ontario family physicians. Ann Fam Med. 2023;21(Supplement 1):3671.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlazier RH, Green ME, Frymire E, Kopp A, Hogg W, Premji K, et al. Do incentive payments reward the wrong providers? A study of primary care reform in Ontario, Canada. Health Aff. 2019;38(4):624\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark B, Gold SB, Bazemore A, Liaw W. How evolving United States payment models influence primary care and its impact on the quadruple aim. J Am Board Fam Med. 2018;31(4):588\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanadian Medical Association. 2018; Number of physicians by province/territory and specialty, Canada, 2018 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cma.ca/sites/default/files/pdf/Physician%20Data/01-physicians-by-specialty-province-e.pdf]\u003c/span\u003e\u003cspan address=\"https://www.cma.ca/sites/default/files/pdf/Physician%20Data/01-physicians-by-specialty-province-e.pdf]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed January 02 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Praet E, Van Leuven S. When change readiness spirals down: a mixed-method case study of change communication at a European government agency. Int J Bus Communication. 2022;59(1):104\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModaresnezhad M, Andrews MC, Mesmer-Magnus J, Viswesvaran C, Deshpande S. Anxiety, job satisfaction, supervisor support and turnover intentions of mid-career nurses: a structural equation model analysis. J Nurs Adm Manag. 2021;29(5):931\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikalef P, Krogstie J, Pappas IO, Pavlou P. Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manag. 2020;57(2):103169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEva N, Meacham H, Newman A, Schwarz G, Tham TL. Is coworker feedback more important than supervisor feedback for increasing innovative behavior? Hum Resour Manag. 2019;58(4):383\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDash G, Paul J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol Forecast Soc Chang. 2021;173:121092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C. PLS path modeling. Comput Stat Data Anal. 2005;48(1):159\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair J, Alamer A. Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: guidelines using an applied example. Res Methods Appl Linguistics. 2022;1(3):100027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao XS, Lynch JG, Chen QM. Reconsidering Baron and Kenny: myths and truths about mediation analysis. J Consum Res. 2010;37(2):197\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWetzels M, Odekerken-Schroder G, van Oppen C. Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 2009;33(1):177\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantini ZI, Jose PE, Cornwell EY, Koyanagi A, Nielsen L, Hinrichsen C, et al. Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. Lancet Public Health. 2020;5(1):E62\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeston R, Gore PA. A brief guide to structural equation modeling. Couns Psychol. 2006;34(5):719\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacCallum RC, Widaman KF, Zhang SB, Hong SH. Sample size in factor analysis. Psychol Methods. 1999;4(1):84\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Soumya R. Partial Least Squares Structural Equation Modeling (PLS-SEM) using R: a workbook. Cham: Springer; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43(1):115\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh HW, L\u0026uuml;dtke O, Trautwein U, Morin AJS. Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Struct Equation Modeling-a Multidisciplinary J. 2009;16(2):191\u0026ndash;225.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpurk D, Hirschi A, Wang M, Valero D, Kauffeld S. Latent profile analysis: a review and how to guide of its application within vocational behavior research. J Vocat Behav. 2020;120:103445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherer R, Howard SK, Tondeur J, Siddiq F. Profiling teachers' readiness for online teaching and learning in higher education: who's ready? Comput Hum Behav. 2021;118:106675.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res. 2011;18(2):52\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHopf YM, Francis J, Helms PJ, Haughney J, Bond C. Core requirements for successful data linkage: an example of a triangulation method. BMJ Open. 2016;6(10):e011879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvers NM, Grimshaw JM, Jamtvedt G, Flottorp S, O\u0026rsquo;Brien MA, French SD, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaneman N, Lee S, Bai HM, Bell CM, Bronskill SE, Campitelli MA, et al. Behavioral nudges to improve audit and feedback report opening among antibiotic prescribers: a randomized controlled trial. Open Forum Infect Dis. 2022;9(5):ofac111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevoy E, Vilendrer S, Dang R, Nasr AS, Luu J, Tawfik D, et al. Physician perspectives of clinical performance feedback and impact on well-being: a qualitative exploration. BMJ Open. 2024;14(11):e082726.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis DA, Mazmanian PE, Fordis M, Van Harrison R, Thorpe KE, Perrier L. Accuracy of physician self-assessment compared with observed measures of competence: a systematic review. JAMA. 2006;296(9):1094\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayar SK, Musto L, Baruah G, Fernandes R, Bharathan R. Self-assessment of surgical skills: a systematic review. J Surg Educ. 2020;77(2):348\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKluger AN, Nir D. The feedforward interview. Hum Resource Manage Rev. 2010;20(3):235\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal SD, Pabo E, Rozenblum R, Sherritt KM. Professional dissonance and burnout in primary care a qualitative study. JAMA Intern Med. 2020;180(3):395\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer RD, Dalal RS, Hermida R. A review and synthesis of situational strength in the organizational sciences. J Manag. 2010;36(1):121\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaddawi-Konefka D, Schumacher DJ, Baker KH, Charnin JE, Gollwitzer PM. Changing physician behavior with implementation intentions: closing the gap between intentions and actions. Acad Med. 2016;91(9):1211\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMastikhina L, Li J, Taplin J, Egunsola O, Dowsett L, Noseworthy T, et al. Large-scale implementation of physician audit and feedback. University of Calgary Health Technology Assessment Unit. Produced for the Strategic Clinical Networks, Alberta Health Services. The Health Technology Assessment Unit, University of Calgary; 2020. pp. 1\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotthoff S, Presseau J, Sniehotta FF, Johnston M, Elovainio M, Avery L. Planning to be routine: habit as a mediator of the planning-behaviour relationship in healthcare professionals. Implement Sci. 2017;12:24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"implementation-science-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iscm","sideBox":"Learn more about [Implementation Science Communications](https://implementationsciencecomms.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ISCM/default.aspx","title":"Implementation Science Communications","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6769454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6769454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Clinical performance feedback (CPF) is widely used to support physician development and improve care. Yet, its impact remains limited by low voluntary engagement. This study sought to: (1) develop a theory-informed model outlining the key beliefs that shape physician engagement with CPF; (2) explore patterns of feedback orientation across physicians; and (3) understand how individual perceptions influence engagement with CPF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe used a cross-sectional, multi-method approach combining a survey and qualitative interviews with primary care physicians in Ontario, Canada. We validated a conceptual model using path analysis, explored heterogeneity in feedback orientation using latent profile analysis, and qualitatively examined how perceptions of CPF influenced engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSurvey results (n=206) supported a model in which engagement with CPF is shaped by five recipient characteristics: perceived need for change (change discrepancy), perceived value of CPF, confidence to act on feedback (feedback self-efficacy), belief that feedback is useful (feedback utility), and sense of responsibility to act (feedback accountability). Perceived utility mediated the effects of self-efficacy and value on accountability, and perceived need for change influenced value. Latent profile analysis identified three groups: physicians with high and balanced feedback orientation (n=32), moderate and balanced (n=143), and low feedback orientation with low self-efficacy (n=31). Interview findings (n=9) revealed two mindsets: physicians who saw value in CPF despite its limitations (engagers), and those who dismissed its relevance (non-engagers). These mindsets aligned with differences in value, utility, and accountability scores from the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eEngagement with CPF is not one-size-fits-all. Physicians differ in how they appraise and act on feedback based on their beliefs about its relevance, usefulness, and their ability to act. CPF initiatives should explicitly link feedback to improved patient outcomes, focus on future actions, and provide clear, actionable guidance. Designing CPF that accounts for recipient heterogeneity is essential to realizing its full potential as an improvement strategy.\u003c/p\u003e","manuscriptTitle":"The engagement equation: A model for understanding what drives physician engagement with data-driven clinical performance feedback","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:31:59","doi":"10.21203/rs.3.rs-6769454/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-06-25T07:40:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T12:17:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-30T01:57:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Implementation Science Communications","date":"2025-05-28T11:17:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"implementation-science-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iscm","sideBox":"Learn more about [Implementation Science Communications](https://implementationsciencecomms.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ISCM/default.aspx","title":"Implementation Science Communications","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9ef5798-0442-4bf5-9cd4-802bb685b608","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:02:06+00:00","versionOfRecord":{"articleIdentity":"rs-6769454","link":"https://doi.org/10.1186/s43058-025-00819-5","journal":{"identity":"implementation-science-communications","isVorOnly":false,"title":"Implementation Science Communications"},"publishedOn":"2025-12-11 15:57:51","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-06-29 14:31:59","video":"","vorDoi":"10.1186/s43058-025-00819-5","vorDoiUrl":"https://doi.org/10.1186/s43058-025-00819-5","workflowStages":[]},"version":"v1","identity":"rs-6769454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6769454","identity":"rs-6769454","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}