{"paper_id":"3d358b29-5e24-4446-8b4e-9bc9f9b9287e","body_text":"Prevalence, Predictors and Outcomes of Feedback for Ems Professionals: a Mixed-methods Diary Study | 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 Prevalence, Predictors and Outcomes of Feedback for Ems Professionals: a Mixed-methods Diary Study Caitlin Wilson, Luke Budworth, Gillian Janes, Rebecca Lawton, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4014306/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Sep, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted 16 You are reading this latest preprint version Abstract Background Providing feedback to healthcare professionals and organisations on performance or patient outcomes may improve care quality and professional development, particularly in Emergency Medical Services (EMS) where professionals make autonomous, complex decisions and current feedback provision is limited. This study aimed to determine the content and outcomes of feedback in EMS by measuring feedback prevalence, identifying predictors of receiving feedback, categorising feedback outcomes and determining predictors of feedback efficacy. Methods An observational mixed-methods study was used. EMS professionals delivering face-to-face patient care in the United Kingdom’s National Health Service completed a baseline survey and diary entries between March-August 2022. Diary entries were event-contingent and collected when a participant identified they had received feedback. Self-reported data were collected on feedback frequency, environment, characteristics and outcomes. Feedback environment was measured using the Feedback Environment Scale. Feedback outcomes were categorised using hierarchical cluster analysis. Multilevel logistic regression was used to assess which variables predicted feedback receipt and efficacy. Qualitative data were analysed using content analysis. Results 299 participants completed baseline surveys and 105 submitted 538 diary entries. 215 (71.9%) participants had received feedback in the last 30 days, with patient outcome feedback the most frequent (n = 149, 42.8%). Feedback format was predominantly verbal (n = 157, 73.0%) and informal (n = 189, 80.4%). Significant predictors for receiving feedback were a paramedic role (aOR 3.04 [1.14, 8.00]), a workplace with a positive feedback-seeking culture (aOR 1.07 [1.04, 1.10]) and white ethnicity (aOR 5.68 [1.01, 29.73]). Diary entries reported feedback as very useful (median 6, IQR 5–7). Feedback outcomes included: personal wellbeing (closure, confidence and job satisfaction), professional development (clinical practice and knowledge) and service outcomes (patient care and patient safety). Feedback-seeking behaviour and higher scores on the Feedback Environment Scale were statistically significant predictors of feedback efficacy. Solicited feedback improved wellbeing (aOR 3.35 [1.68, 6.60]) and professional development (aOR 2.58 [1.10, 5.56]) more than unsolicited feedback. Conclusion Feedback for EMS professionals was perceived to improve personal wellbeing, professional development and service outcomes. EMS workplaces need to develop a culture that encourages feedback-seeking to strengthen the impact of feedback for EMS professionals on clinical decision-making and staff wellbeing. Feedback prehospital care emergency medical services professional development staff wellbeing diary methods multilevel modelling BACKGROUND The National Health Service (NHS) staff survey [ 1 ] consistently identifies Emergency Medical Services (EMS) professionals as the group with the highest work-related stress (55.7%), burnout (49.3%) and leaving intentions (42.9%) – with ~ 25% having applied for non-NHS jobs post COVID-19 [ 2 ]. Receiving feedback on patient outcomes and personal performance may improve job support for EMS professionals and enhance staff wellbeing, job satisfaction and patient care [ 3 , 4 ]. Across healthcare settings, including EMS, clinical performance feedback has been demonstrated to improve quality of care and professional development [ 5 , 6 ]. However, recent reviews of existing literature [ 6 ] and current practice [ 7 ] recommend further research on the provision of patient outcome feedback and the impact of feedback on staff wellbeing in EMS. EMS professionals could particularly benefit from feedback as their work environment is characterised by complexity, uncertainty and extreme stressors [ 8 , 9 ]. EMS professionals work autonomously, making complex decisions including assessing and treating patients at home to avoid unnecessary hospital attendance and reduce demand on emergency departments [ 10 , 11 ]. Nevertheless, providing and accessing, EMS feedback on decision-making is difficult due to constraints such as a mobile workforce, disconnected digital technology [ 12 ] and data sharing governance issues [ 4 ]. When feedback is provided for EMS professionals this is typically through formal initiatives, such as performance feedback during appraisals, patient outcome feedback from “post-box” schemes and patient-experience feedback through thank-you letters [ 3 , 7 ]. However, qualitative research suggests that EMS professionals desire more and better feedback, especially concerning patient outcomes [ 3 , 4 , 13 ]. When formal feedback initiatives are lacking, EMS professionals informally approach ED staff seeking feedback on patient outcomes [ 3 ]. However, informal feedback is limited by patient confidentiality issues, information quality, verbal format and geographical barriers [ 4 , 13 ]. While systematic reviews [ 6 ] and current practice [ 7 ] suggest formal feedback to EMS professionals positively affects patient care and clinical performance, it is unknown whether informal feedback or actively solicited feedback have similar outcomes. In the United States (US), it is estimated that feedback is provided to EMS professionals in just 24% of encounters [ 14 ] with 50–69% of paramedics self-reporting having received feedback in the previous month [ 15 , 16 ]. Particular recipient and contextual characteristics appear associated with increased feedback, including staff with higher level certifications, fewer years’ experience and working in busier or hospital-based organisations [ 15 ]. Learning more about how the context and format of feedback impacts outcomes, as well as the mechanisms through which feedback influences outcomes, could be an important step in enhancing feedback effectiveness in EMS [ 17 ]. In this vein, Clinical Performance Feedback Intervention Theory, which has good face validity in the prehospital setting [ 3 ], offers 42 hypotheses of when feedback is more effective e.g. when feeding back to staff with positive beliefs about feedback [ 18 ]. Feedback effectiveness is also predicted by the extent to which an organisation encourages, provides and uses feedback, i.e. the ‘feedback environment’ [ 19 , 20 ], whereby a positive feedback environment predicts positive outcomes for individuals and organisations [ 21 – 24 ]. Despite increasing research interest in prehospital feedback, no studies have explored the content and outcomes of prehospital feedback prospectively, or assessed feedback prevalence and predictors amongst EMS professionals in the United Kingdom. International studies have been limited by not drawing upon existing theory and potential recall bias [ 15 , 16 ]. This study aimed to address these gaps by answering the following research questions: How prevalent is feedback for UK EMS professionals and what types of feedback do they receive? What individual and contextual factors predict EMS professionals receiving feedback in the previous 30 days? What are the perceived outcomes of feedback for EMS professionals? What predicts instances of feedback being perceived as improving outcomes? METHODS Study design This observational mixed-methods study consisted of a baseline survey followed by diary entries. Collecting diary entries in real time is known to reduce recall bias by collecting data at the level of feedback events and therefore not relying on generalised reflections of feedback provision over a period of time, whilst enabling analysis of within- and between-person variability [ 25 ]. Diary entries were event-contingent and collected when a participant identified they had received feedback. Diary entries on desired feedback and a follow-up survey were part of the study but are not reported here. Open-ended questions were used to contextualise and expand upon quantitative results [ 26 ]. Ethical approval was granted from the University of Leeds ethics committee (PSYC-406 04/01/2022) and the Health Research Authority (ID: 295645). STROBE [ 27 ] and LEVEL recommendations [ 28 ] were followed. Setting and selection of participants Eligible participants were EMS clinicians (i.e. paramedics) and non-registered professionals (e.g. emergency medical technicians) delivering face-to-face patient care, employed by an NHS ambulance trust in the United Kingdom. An opportunistic sample was recruited via social media and organisations’ internal communications. Informed consent was obtained in the baseline survey after providing study information. Access to the baseline survey was via an anonymous link, with individual diary study links issued to participants who provided their email address in their survey response. Participants completing all study elements were enrolled in a prize draw for three £50 vouchers to aid recruitment and reduce drop-out. Data collection Data was collected using Qualtrics (Qualtrics, Provo, UT) (March-August 2022). The survey and diary study measures were developed for this study (Additional file 1). They were piloted with three EMS professionals and refined based on their feedback. Baseline survey The baseline survey covered demographics, feedback frequency and feedback environment. Demographic questions included professional role, years of EMS experience, sex, age and ethnicity. The feedback frequency questions were adapted from a large-scale US EMS feedback survey [ 15 ]. They included items such as ‘In the past 30 days, did you receive any feedback on the medical care you provided to a patient?’ scored on a dichotomous scale (‘yes/no’). If answered positively, it was followed by ‘How was this feedback provided? Verbal, by email, by text, written on paper, other’. The feedback environment measure was based upon the shortened Feedback Environment Scale (FES) [ 29 ], which demonstrated excellent reliability for nurses (Cronbach’s alpha 0.90) [ 30 ]. The questions were adapted for the prehospital setting and reworded so as not to refer to a specific feedback source. Participants were asked to respond on a Likert-type scale ranging from 1–7 (strongly disagree-strongly agree) to statements such as ‘I receive useful feedback at work’ and ‘When I want feedback, this is readily available’. Once respondents provided ratings for each of the 14 items, the scores were aggregated. A high score on the FES generally indicates a positive perception of the feedback environment [ 29 ]. Diary entries Immediately after completing the baseline survey, participants were sent a link to access their diary which remained open until the end of the data collection period. When logging a feedback event, participants were asked a series of multiple choice and structured response questions informed by Clinical Performance Feedback Intervention Theory [ 18 ], including, for example, ‘How quickly after the incident was the feedback provided?’ and ‘What effect do you think receiving this feedback had on your clinical practice/knowledge/confidence/sense of closure/job satisfaction/patient care/patient safety? Positive, negative or no effect’. Data analysis Quantitative analyses was undertaken in R (Version 4.1.3, R Core Team) [ 31 ] within RStudio [ 32 ] and qualitative analyses in NVivo (Version 12 Plus, QSR International). The detailed multilevel data analysis plan [ 33 ], study hypotheses and research models are described in Additional file 2. Study size Assuming 50/50 balanced binary predictors and normally distributed continuous predictors, 325 participants were required to detect any significant predictors of a medium-sized effect (i.e. Cohen’s d = 0.5) for prehospital feedback perceived as improving outcomes with 80% power, after adjustment for other variables [ 34 ]. The level-1 sample size was pre-specified by the research team as 10 diary entries was deemed an acceptable burden for each participant during stakeholder consultation. The power analysis was based on the basic research model, which included two level-2 predictors (role – binary, length in service – continuous) and two level-1 predictors (feedback content – categorical, solicited/unsolicited – binary). Statistical methods Data on the individual-level variables (role, length in service, FES score) were collected during the baseline survey. Data on the diary-level independent variables (feedback content, feedback-seeking behaviour, formal/informal, source, sign, format, lag-time) and dependent variable (feedback outcome) were collected for each diary entry. FES scale reliability was examined using Cronbach’s alpha. To describe feedback prevalence, descriptive statistics for baseline quantitative data were produced alongside content analysis of free-text qualitative responses. To identify predictors of receiving feedback in the last 30 days, baseline survey data were analysed using binary logistic regression (via ‘lme4’) [ 35 ]. Univariable logistic regression assessed individual associations between each predictor (e.g. role) and the outcome (i.e. having received feedback in the previous 30 days). Multivariable logistic regression included all predictors simultaneously that formed part of the simple or extended research model. To identify predictors of feedback efficacy, data generated via feedback-received diary entries were analysed using multilevel logistic regression with random intercepts to account for multiple recorded feedback instances per participant. The variables of interest were chosen based on Clinical Performance Feedback Intervention Theory [ 18 ] and qualitative exploratory studies of prehospital feedback [ 3 , 7 ], for example feedback type, feedback-seeking behaviour and formal/informal. Continuous variables were grand-mean centred to improve the interpretation of the intercept values by shifting the focus to the effect of deviations from the overall average, rather than deviations from zero [ 36 ]. Akaike Information Criterion (AIC) [ 37 ] was used to compare models with the same outcome based on goodness-of-fit, whereby smaller AIC values indicate better fit. We did not adjust alpha for multiple comparisons due to deliberately favouring a higher Type I error rate relative to the potential for Type II error, as this was an exploratory study and it was deemed more harmful for future work to be conservative in potential leads [ 38 ]. Analyses were conducted using complete cases, followed by sensitivity analyses dealing with missing data using the ‘mice’ R package [ 39 ]. To categorise perceived outcomes of receiving feedback, hierarchical cluster analysis was performed on the baseline data (using ‘ClustOfVar’ [ 40 ]). Cluster analysis is an exploratory analysis that identifies structures within the data and visualises them in a dendrogram (tree diagram) with outcomes that co-occur most frequently placed on branches closer together [ 41 ]. Clusters were labelled by the research team using thematic classification informed by previous research [ 3 , 6 ]. Free-text qualitative responses were analysed using content analysis. RESULTS Characteristics of study participants Two hundred and ninety-nine participants completed the baseline survey representing 13 of the 14 UK ambulance trusts (median 19, range 4–88 participants per trust). Of these, 105 completed 538 feedback-received diary entries (range 1–16, median 4). Table 1 summarises participants’ baseline characteristics. Ethnicity was collapsed into a binary variable (white m = 290, minoritised ethnic group n = 8) to avoid identifying participants. Inferential statistics did not indicate that participants’ characteristics significantly differed between the baseline survey and diary entry stages. Comparison with national data for UK ambulance services [ 42 ] using chi-square tests at 0.05 significance level indicated that our study sample was representative in terms of ethnicity (p = 0.771), sex (p = 0.124) and age (p = 0.886). The FES was found to have excellent internal consistency (alpha = 0.85 [95% CI 0.81 to 0.88]). Of 299 baseline surveys, 77 (25.5%) were incomplete. Missing values varied from 0.3–25.1%. Table 1 Characteristics of study participants Baseline survey Diary entries Number of participants , n 299 105 Role , n (%) Emergency Medical Technician 59 (19.7) 16 (15.2) Paramedic 239 (79.9) 89 (84.8) Age in years, median (IQR) 36 (29.0–45.0) 38 (30.5–45.0) Sex , n (%) Female 120 (40.1) 39 (37.1) Male 177 (59.2) 66 (62.9) Not stated 2 (0.7) 0 (0) Ethnicity , n (%) Minoritised ethnic group 8 (2.7) 2 (1.9) White 290 (97.0) 103 (98.1) Not stated 1 (0.3) 0 (0) Years of work experience , median (IQR) 7 (3.7–13.4) 9 (4.5–14.4) FES score , mean ± SD 53.63 ± 14.22 52.72 ± 13.09 Presence of formal feedback initiative , n (%) Yes 68 (22.7) 26 (24.8) No 231 (77.3) 79 (75.2) Feedback prevalence and types Table 2 describes the characteristics of feedback prevalence from the baseline data and diary entries. Of the 299 participants completing the baseline survey, 215 (71.9%) indicated that they had received feedback in the last 30 days, with patient outcome feedback being the most frequently received (n = 149, 42.8%). Feedback was predominantly provided in verbal format (n = 157, 73.0%) and was informal (n = 189, 80.4%). Diary entries indicated that the median perceived usefulness of feedback was 6 (IQR 5–7). Table 2 Characteristics of feedback prevalence at baseline and during diary study Baseline feedback received (N = 215), n(%) Diary entries feedback received (N = 538), n(%) Type Patient outcome feedback 149 (42.8) 226 (42.0) Patient experience feedback 88 (25.3) 108 (20.1) Clinical performance feedback 111 (31.9) 201 (37.4) Incident-prompted feedback - 2 (0.4) Post-event debriefing - 1 (0.2) Source (multiple selections possible) Non-ambulance healthcare professionals 114 (33.9) 202 (37.5) EMS professionals or managers 132 (39.3) 180 (33.5) Patients/relatives 85 (25.3) 147 (27.3) Other 5 (1.5) 9 (1.7) Electronic 41 (19.1) 90 (16.7) Verbal 157 (73.0) 414 (77.0) Written 16 (7.4) 30 (5.6) Other 1 (0.5) 4 (0.7) Lag time Immediate or within 1 day 130 (45.8) 370 (68.8) 2–3 days 53 (18.7) 45 (8.4) 4–7 days 27 (9.5) 41 (7.6) 8–14 days 28 (9.9) 24 (4.5) More than 14 days 46 (16.2) 58 (10.8) Feedback-seeking behaviour Unsolicited 143 (53.6) 335 (62.3) Solicited 124 (46.4) 203 (37.7) Formal/informal Formal 46 (19.6) 87 (16.2) Informal 189 (80.4) 451 (83.8) Sign (i.e. nature or direction of feedback) Positive Not collected 383 (71.6) Neutral Not collected 91 (17.0) Negative Not collected 16 (3.0) Mixed Not collected 45 (8.4) Usefulness 1 – Not useful at all Not collected 4 (0.7%) 2 Not collected 16 (3.0%) 3 Not collected 24 (4.5%) 4 Not collected 70 (13.0%) 5 Not collected 120 (22.3%) 6 Not collected 155 (28.8%) 7 – Extremely useful Not collected 178 (33.1%) Predicted likelihood of receiving feedback The likelihood of receiving feedback in the past 30 days was higher for those with higher FES scores (aOR 1.07 [1.04, 1.10]), meaning that each one-point increase in Feedback Environment Scale increased the odds of receiving feedback by 7% (see Table 3 ). Participants in paramedic roles had three times the estimated odds of receiving feedback than non-registered EMS professionals (aOR 3.04 [1.14, 8.00]). Those of white ethnicity had five times the estimated odds of receiving feedback compared with minoritised ethnic group participants (aOR 5.68 [1.01, 29.73]); although, the wide confidence interval indicates a high level of uncertainty in this estimate. The sensitivity analysis (Additional file 3) indicated that when missing data was imputed, ethnicity did not predict the likelihood of receiving feedback (aOR 3.34 [0.71, 15.71]). Table 3 Factors associated with receiving feedback in the past 30 days Univariable OR (95% CI), p-value Multivariable aOR (95% CI), p-value FES (continuous) 1.06 (1.04, 1.09)*, p < 0.001* 1.07 (1.04, 1.10), p < 0.001* Role (binary) (ref = Emergency Medical Technician) Paramedic 1.65 (0.70, 3.65), p = 0.231 3.04 (1.14, 8.00), p = 0.024* Sex (binary) (ref = Female) Male 1.12 (0.56, 2.18), p = 0.750 1.14 (0.54, 2.39), p = 0.723 Ethnicity (binary) (ref = Minoritised ethnic group) White 3.07 (0.59, 14.44), p = 0.152 5.68 (1.01, 29.73), p = 0.037* Years of work experience (continuous) 0.97 (0.93, 1.01), p = 0.107 0.98 (0.92, 1.04), p = 0.523 Age (continuous) 0.97 (0.94, 1.00), p = 0.069 0.98 (0.94, 1.03), p = 0.411 Existence of feedback initiative (binary) (ref = no) Yes 1.25 (0.58, 2.96), p = 0.584 1.07 (0.45, 2.74), p = 0.884 Outcomes of feedback Feedback outcomes were categorised into three clusters following a visual inspection of the dendrogram from the hierarchical cluster analysis and stability of the partitions (Additional file 4). Cluster 1 (‘ professional development ’) encompassed clinical practice and knowledge, Cluster 2 (‘ personal wellbeing’ ) encompassed closure, confidence and job satisfaction, and Cluster 3 (‘ service outcomes’ ) encompassed patient care and patient safety. Table 4 describes the proportion of positive, negative, mixed and no impact within each feedback outcome cluster. Table 4 Perceived impact within each feedback outcome cluster Professional development n (%) Personal wellbeing n (%) Service outcomes n (%) Positive impact 434 (80.7) 475 (88.3) 405 (75.3) No impact 95 (17.7) 15 (2.8) 115 (21.4) Negative impact 8 (1.5) 33 (6.1) 12 (2.2) Mixed impact 1 (0.2) 15 (2.8) 6 (1.1) Overall, feedback was reported to have a positive impact. This was even more apparent for feedback that had personal wellbeing implications (88.3%). Free-text comments described positive impacts as making participants feel “ good ” or “ great ” (n = 126), “ satisfied that they had done a good job ” (n = 91), “ happy ” or “ pleased ” (n = 88), “ motivated ” (n = 70), “ appreciated ” (n = 53) and “ thankful ” (n = 15). However, feedback in this category also had the highest percentage of negative impacts (6.1% vs 1.5 and 2.2%) and very rarely had no impact at all (2.8%). Negative impacts were further described by participants as being “ sad ” (n = 18), “ annoyed ” (n = 22) and feeling “ defensive ” (n = 18), for example when the “ [prehospital] patient presentation was completely different to [the hospital] diagnosis ” (PID 118). One participant described that they were “ nervous, questioned [my] practice, and concerned that I may not have done [the] job properly ” (PID 177). Another participant recalled feeling “ deflated, angry, and disappointed ” (PID 74). The 33 feedback events resulting in negative affective responses were reported by 25 participants, who had lower FES scores and received punitive feedback that was predominantly negative, unsolicited and provided by EMS professionals. No impact was most commonly recorded for service outcomes. This was echoed in the baseline survey, in which only 1 of the 299 participants (0.3%) indicated that the feedback they had received in the past 30 days resulted in a change to professional practice across the organisation. According to the survey data, changes at a team level were also rare (n = 5, 1.7%), with the two free-text responses indicating it changed how the team “ approach[ed] certain cases ” (PID 172) and “ non-conveyances rates ” (PID 103). In contrast, nearly 1 in every 5 participants (n = 54, 18.1%) reported changes at an individual level. Examples included developing “ a more structured approach to history taking ” (PID 42, 99, 237), improving documentation (PID 199, 261, 107, 184, 285) and having a greater awareness of how unusual conditions present (PID 71, 156, 187). Eight participants described that feedback had changed their clinical decision-making and that they would subsequently manage similar cases differently (PID 54, 93, 128, 193, 197, 198, 235, 258). Predicted likelihood of feedback efficacy Additional file 5 summarises the results of the univariable and multivariable multilevel analyses identifying predictors of feedback efficacy. Sensitivity analyses (Additional file 6) indicated that missing data had some effect in the univariable analyses but little effect in the multivariable multilevel analyses. The ICC Professional =0.25, ICC Personal =0.19 and ICC Service =0.24 indicated that between 19% − 25% of the variance was explained by between-participant differences. This means that a moderate amount of the variablity in feedback having a positive impact on professional development, personal wellbeing and service outcomes, was explained at a participant level, rather than at the level of individual feedback events. Comparing the AICs for the basic and extended research model suggested that the extended research model was the best fit for all three outcome clusters. The extended research model indicated that feedback-seeking behaviour and FES were statistically significant predictors of feedback efficacy. Solicited feedback was more likely to improve professional development (aOR 3.35 [1.68, 6.69]) and personal wellbeing (aOR 2.58 [1.19, 5.56]) than unsolicited feedback. A one-point increase in FES led to a predicted 4% increase in the odds of feedback positively affecting personal wellbeing (aOR 1.04 [1.01, 1.07]) and a 3% increase for service outcomes (aOR 1.03 [1.00, 1.06]). Results from the multilevel univariable analyses suggested that improvements in professional development were less likely with patient-experience feedback than patient outcome or clinical performance feedback (OR 0.38 [0.20, 0.75]), but this was not replicated in the sensitivity analysis indicating that missing data had an effect on this predictor. Feedback from healthcare professionals outside of the ambulance service had almost five times the estimated odds of positively affecting professional development than feedback from other sources (OR 4.93 [2.93, 8.29]). Negative feedback was less likely to improve professional development than positive feedback (OR 0.18 [0.04, 0.73]). Similarly, negative feedback was much less likely to improve personal wellbeing (OR 0.12 [0.02, 0.56]), whilst positive feedback had over thirty times the estimated odds of positively affecting personal wellbeing (OR 31.0 [11.4, 84.3]). Improvements to personal wellbeing was most likely if feedback was provided by patients or relatives than other sources (OR 3.36 [1.45, 7.80]). The univariable analyses for service outcomes did not indicate any statistically significant results. DISCUSSION In total, 215 (71.9%) participants indicated that they had received feedback in the last 30 days with patient outcome feedback most received (n = 149, 42.8%). Significant predictors for receiving feedback were a paramedic role and a workplace with a positive feedback-seeking culture. Participants reported that feedback affected personal wellbeing (closure, confidence, job satisfaction), professional development (clinical practice, knowledge) and service outcomes (patient care, patient safety). Solicited or positive feedback was more likely to positively affect personal and professional development than unsolicited or negative feedback. Compared to US studies, our participants reported a slightly higher prevalence of receiving feedback in the past 30 days: 71.9% compared to 50.0% [ 16 ] and 69.4% [ 15 ]. This could be because our study provided clearer specification of feedback through definitions provided to participants. Consistent with other studies, feedback was mostly received in verbal format (73.0%) and provided by a mixture of EMS professionals (39.3%), non-ambulance healthcare professionals (33.9%) and patients or relatives (25.3%) [ 15 , 16 ]. Patient outcome feedback was the type most frequently received by our participants (42.8%), which differs from the largest US study on this topic in which receipt of clinical performance feedback dominated [ 15 ]. In contrast to previous studies of prehospital feedback [ 14 – 16 ], years of experience were not a significant predictor of receiving feedback in our study. However, we did identify several novel predictors of receiving feedback, such as paramedic role and a workplace with a positive feedback culture as indicated by high FES scores. Paramedics may receive more feedback compared with non-registered EMS professionals because they take the lead on more acute cases and are therefore in a better position to actively seek feedback, as indicated by 38.6% (n = 180) of feedback for paramedics being solicited compared with only 31.9% (n = 23) for emergency medical technicians. It may also be that paramedics have become used to receiving enhanced feedback during undergraduate training or the newly qualified paramedic period and are therefore continuing to seek enhanced feedback provision [ 3 ]. The broader feedback literature offers theoretical support regarding feedback exchanges being affected by social categories such as race, gender, age and sexual orientation, in that staff with minority characteristics are less likely to actively seek feedback [ 43 ]. Further understanding how personal characteristics influence EMS feedback interactions is vital to promote equity and inclusion within feedback theory and practice. Our analysis indicates that solicited feedback was more likely to improve professional development and personal wellbeing than unsolicited feedback. This may be due to solicited feedback being timelier, more relevant and originating from a more credible source as the recipient has some control over whom they approach, compared with unsolicited feedback. Overall this probably reflects the limitations of the existing prehospital feedback provision in regards to timeliness, relevance and credibility, rather than solicited feedback being an ultimate desirable goal [ 7 ]. The positive effects of prehospital feedback on quality of care and professional development were synthesised in a recent systematic review [ 6 ], but EMS professionals in our study also perceived that feedback positively affects personal outcomes such as closure (68.8%), confidence (83.1%) and job satisfaction (81.8%). This confirms suggestions from qualitative and survey studies that feedback for EMS professionals can support staff wellbeing and job satisfaction [ 3 , 4 , 7 , 16 ]. Feedback made our study participants feel they were “ part of a patient’s journey ” (PID 196) and as if they “ had made a difference ” (PID 113, PID 143). Our study also highlights the importance of feedback delivery as demonstrated by negative impacts not only being related to a negative patient outcome, but also by feedback being perceived as “ good, but also patronising a little ” (PID 55), participants feeling “ uncomfortable ” due to the way feedback was delivered (PID 73) and feedback being perceived as “ not genuine ” (PID 215) or having “ no sincerity ” (PID 215). Credibility of the feedback source and content has been identified as influencing feedback effectiveness in the broader audit and feedback literature [ 5 ] [ 44 ]. Implications for research and practice Further research should include developing theory-informed measures to evaluate how prehospital feedback initiatives impact professional practice, personal wellbeing and service outcomes. Observational studies within EMS should be conducted to deepen our understanding of solicited and unsolicited feedback, the utility of negative feedback and the influence of personal characteristics on EMS feedback interactions and engagement. A particular area in need of further research are minoritised ethnic EMS professionals. Further research should also focus on what feedback EMS professionals want to receive. Change in clinical practice should focus on designing and robustly evaluating feedback provision for EMS professionals. All EMS professionals should be enabled to make better use of the feedback they have access to. Non-registered EMS professionals should be supported to actively seek feedback to address the current feedback inequity, which places them at a disadvantage when it comes to development of professional competency and performance. Care should be taken in feeding back service level outcomes to frontline EMS professionals to ensure that the feedback is relevant and actionable at their level. Tailoring feedback interventions to support personal wellbeing is most likely to be perceived by EMS professionals to have positive impacts than those targeting professional development or service outcomes. The benefits of feedback for staff wellbeing should be formally recognised by ambulance services given the potential to mitigate workforce challenges, such as burnout, retention and recruitment. Feedback targeting personal wellbeing may also do harm and organisations should adequately support EMS professionals when receiving feedback. Strengths and Limitations This was the first study to assess feedback prevalence within the UK EMS population and to explore the associated contextual factors and outcomes. This study was limited by the high drop-out rate (n = 299 participants at baseline, n = 105 logging diary entries), though this is typical of diary studies generally [ 45 ]. To combat high dropout in future diary studies, researchers could offer greater incentives or further reduce survey length. However, using diary methods was a novel way to assess feedback prevalence that reduced recall bias and provided reliable within-person data. Testing for differences between the prospective diary entries and retrospective baseline data to quantify recall bias indicated significantly shorter lag times (p < 0.001) and a higher proportion of unsolicited feedback (p = 0.018) for the prospectively collected data, suggesting that retrospective data collection may not be reliable for feedback in EMS. Despite data collection taking place during the early post-pandemic period when the backlog of health needs were emerging, the large number of NHS staff that participated and feedback events that were reported, indicate an appetite for feedback research from EMS professionals. However, this study was unable to recruit to target. Challenges related to the demanding schedules and limited availability for research participation of the target NHS staff group, combined with reliance on voluntary participation, are likely to have contributed to the relatively low response rate. Future research should explore alternative recruitment strategies to enhance participation rates within this professional context. Comparison with national data for UK ambulance services [ 42 ] indicated that our study sample was representative of UK EMS but it remains unclear to what extent these findings might be replicated in the health systems of other countries. We acknowledge that collapsing our ethnicity variable into binary categories limits our conclusions regarding specific ethnic minority groups. The divergence between the complete case analysis and the multiple imputation sensitivity analysis regarding whether ethnicity predicted the likelihood of receiving feedback suggests this predictor may not be very robust. However, as feedback is mostly positive, this is a potential inequality and needs further investigation. Future studies should specifically target minority group participation, particularly as the literature suggests that social identity and race influence feedback-seeking behaviour [ 43 ]. CONCLUSIONS In conclusion, our study provides valuable insights into the prevalence, predictors and outcomes of feedback provision within the UK EMS context. Our findings underscore the importance of feedback in enhancing not only clinical practice and service outcomes but also personal wellbeing and job satisfaction among EMS professionals. However, the delivery of feedback emerged as a critical factor influencing its effectiveness, highlighting the need for attention to credibility and sensitivity in feedback delivery. Addressing feedback inequities, particularly among non-registered EMS professionals and minoritised groups, is crucial for promoting workforce development and ensuring equitable access to development opportunities. Overall, this study suggests that EMS workplaces need to develop a culture that encourages feedback-seeking by ensuring high-quality positive and negative feedback is readily available and provided by a credible source to strengthen the impact of feedback for EMS professionals on clinical decision-making and staff wellbeing. Abbreviations AIC - Akaike Information Criterion ED – Emergency Department EMS – Emergency Medical Services FES – Feedback Environment Scale NHS – National Health Service OR – Odds Ratio Declarations Ethics approval and consent to participate: The study was carried out in accordance with the UK Policy Framework for Health and Social Care Research (Health Research Authority, 2017) and was approved by the Health Research Authority (IRAS project ID 295645) and the University of Leeds ethics committee (PSYC-406 04/01/2022). Informed consent was obtained in the baseline survey after providing participants with study information. Consent for publication : Not applicable. Availability of data and materials : The datasets generated and analysed during the current study are not publically available as sharing the raw data would violate the agreement to which participants consented; however, the datasets are available from the corresponding author on reasonable request. Competing interests : The authors declare that they have no competing interests. Funding : This research was funded by the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions: CW conceived the study, developed the study protocol, obtained relevant ethics and governance approvals, collected the data, analysed the data and drafted the manuscript under supervision from GJ, RL and JB. LB provided guidance on the statistical analysis plan, sample size calculation and data analysis. CW drafted the article and all authors contributed substantially to its revision and approved the final version. CW takes responsibility for the paper as a whole. Acknowledgments : The authors would like to thank the study participants for taking the time to complete the survey and log diary entries, as well as the research departments of participating ambulance trusts for their support with advertisement and recruitment. Thank you to Professor Helen Snooks, University of Swansea, and Professor Graham Law, University of Lincoln, for peer-reviewing the study protocol. References NHS England: NHS Staff Survey 2022 - National results briefing . In . : NHS; 2023. Weyman A, Glendinning R, O’Hara R, Coster J, Roy D, Nolan P: Should I stay or should I go? NHS staff retention in the post COVID-19 world: Challenges and prospects - IRR Report . In . : University of Bath; 2023. Wilson C, Howell A-M, Janes G, Benn J: The role of feedback in emergency ambulance services: a qualitative interview study . BMC Health Services Research 2022, 22 :296. Eaton-Williams P, Mold F, Magnusson C: Exploring paramedic perceptions of feedback using a phenomenological approach . British Paramedic Journal 2020, 5 (1):7-14. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard‐Jensen J, French SD, O'Brien MA, Johansen M, Grimshaw J, Oxman AD: Audit and feedback: effects on professional practice and healthcare outcomes . Cochrane Database of Systematic Reviews 2012(6). Wilson C, Janes G, Lawton R, Benn J: The types and effects of feedback for emergency ambulance staff: a systematic mixed studies review and meta-analysis . BMJ quality & safety 2023, 32 :573-588. Wilson C, Janes G, Lawton R, Benn J: Feedback for Emergency Ambulance Staff: A National Review of Current Practice Informed by Realist Evaluation Methodology . Healthcare 2023, 11 (16). Fisher JD, Freeman K, Clarke A, Spurgeon P, Smyth M, Perkins GD, Sujan MA, Cooke MW: Health Services and Delivery Research . In: Patient safety in ambulance services: a scoping review. edn. Southampton (UK): NIHR Journals Library; 2015. Lawn S, Roberts L, Willis E, Couzner L, Mohammadi L, Goble E: The effects of emergency medical service work on the psychological, physical, and social well-being of ambulance personnel: a systematic review of qualitative research . BMC Psychiatry 2020, 20 (1):348. Paulin J, Kurola J, Koivisto M, Iirola T: EMS non-conveyance: A safe practice to decrease ED crowding or a threat to patient safety? BMC Emerg Med 2021, 21 (1):115. Blodgett JM, Robertson DJ, Pennington E, Ratcliffe D, Rockwood K: Alternatives to direct emergency department conveyance of ambulance patients: a scoping review of the evidence . Scand J Trauma Resusc Emerg Med 2021, 29 (1):4. Porter A, Badshah A, Black S, Fitzpatrick D, Harris-Mayes R, Islam S, Jones M, Kingston M, LaFlamme-Williams Y, Mason S et al : Electronic health records in ambulances: the ERA multiple-methods study . Health Serv Deliv Res 2020, 8 (10). Morrison L, Cassidy L, Welsford M, Chan TM: Clinical Performance Feedback to Paramedics: What They Receive and What They Need . AEM Education and Training 2017, 1 (2):87-97. Mock EF, Wrenn KD, Wright SW, Eustis TC, Slovis CM: Feedback to emergency medical services providers: the good, the bad, and the ignored . Prehospital and disaster medicine 1997, 12 (2):145-148. Cash RE, Crowe RP, Rodriguez SA, Panchal AR: Disparities in feedback provision to emergency medical services professionals . Prehospital Emergency Care 2017, 21 (6):773-781. McGuire SS, Luke A, Klassen AB, Myers LA, Mullan AF, Sztajnkrycer MD: It’s Time to Talk to Prehospital Providers: Feedback Disparities among Ground-Based Emergency Medical Services Providers and its Impact on Job Satisfaction . Prehospital and disaster medicine 2021, 36 (4):486-494. Hysong SJ, Kell HJ, Petersen LA, Campbell BA, Trautner BW: Theory-based and evidence-based design of audit and feedback programmes: examples from two clinical intervention studies . BMJ quality & safety 2017, 26 (4):323. Brown B, Gude WT, Blakeman T, van der Veer SN, Ivers N, Francis JJ, Lorencatto F, Presseau J, Peek N, Daker-White G: 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 . Implementation Science 2019, 14 (1):40. Rife GL: The influence of feedback orientation and feedback environment on clinician processing of feedback from client outcome measures . University of Akron; 2016. London M, Smither JW: Feedback orientation, feedback culture, and the longitudinal performance management process . Human Resource Management Review 2002, 12 (1):81-100. Norris-Watts C, Levy PE: The mediating role of affective commitment in the relation of the feedback environment to work outcomes . Journal of Vocational Behavior 2004, 65 (3):351-365. Rosen CC, Levy PE, Hall RJ: Placing perceptions of politics in the context of the feedback environment, employee attitudes, and job performance . Journal of Applied Psychology 2006, 91 (1):211-220. Sparr JL, Sonnentag S: Feedback environment and well-being at work: The mediating role of personal control and feelings of helplessness . European Journal of Work and Organizational Psychology 2008, 17 (3):388-412. Whitaker BG, Dahling JJ, Levy P: The Development of a Feedback Environment and Role Clarity Model of Job Performance . Journal of Management 2007, 33 (4):570-591. Bolger N, Davis A, Rafaeli E: Diary methods: capturing life as it is lived . Annual review of psychology 2003, 54 :579-616. Creswell JW, Plano Clark VL: Designing and conducting mixed methods research . Thousand Oaks, Calif.: SAGE Publ.; 2006. Elm Ev, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP: Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies . 2007, 335 (7624):806-808. Monsalves MJ, Bangdiwala AS, Thabane A, Bangdiwala SI: LEVEL (Logical Explanations & Visualizations of Estimates in Linear mixed models): recommendations for reporting multilevel data and analyses . BMC Medical Research Methodology 2020, 20 (1):3. Steelman LA, Levy PE, Snell AF: The Feedback Environment Scale: Construct Definition, Measurement, and Validation . Educational and Psychological Measurement 2004, 64 (1):165-184. Giesbers APM, Schouteten RLJ, Poutsma E, van der Heijden BIJM, van Achterberg T: Towards a better understanding of the relationship between feedback and nurses’ work engagement and burnout: A convergent mixed-methods study on nurses’ attributions about the ‘why’ of feedback . International Journal of Nursing Studies 2021, 117 . R: A Language and Environment for Statistical Computing [https://www.R-project.org/] Posit team: RStudio: Integrated Development Environment for R. In . Boston, MA: Posit Software, PBC; 2023. Sommet N, Morselli D: Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS . International Review of Social Psychology 2017. Olvera Astivia OL, Gadermann A, Guhn M: The relationship between statistical power and predictor distribution in multilevel logistic regression: a simulation-based approach . BMC Medical Research Methodology 2019, 19 (1):97. Bates D, Mächler M, Bolker B, Walker S: Fitting Linear Mixed-Effects Models Using lme4 . Journal of Statistical Software 2015, 67 (1):1 - 48. Centering in Multilevel Regression [http://web.pdx.edu/~newsomj/mlrclass/ho_centering.pdf] Bozdogan H: Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions . Psychometrika 1987, 52 (3):345-370. Gelman A, Hill J, Yajima M: Why We (Usually) Don't Have to Worry About Multiple Comparisons . Journal of Research on Educational Effectiveness 2012, 5 (2):189-211. van Buuren S, Groothuis-Oudshoorn K: mice: Multivariate Imputation by Chained Equations in R . Journal of Statistical Software 2011, 45 (3):1 - 67. Chavent M, Kuentz-Simonet V, Liquet B, Saracco J: ClustOfVar: An R Package for the Clustering of Variables . Journal of Statistical Software 2012, 50 (13):1 - 16. Tullis T, Albert B: Chapter 9 - Special Topics . In: Measuring the User Experience (Second Edition). edn. Edited by Tullis T, Albert B. Boston: Morgan Kaufmann; 2013: 209-236. NHS Digital: NHS Workforce Statistics - June 2022 . In . : NHS Digital; 2022. Flores C, Elicker JD, Cubrich M: The Importance of Social Identity in Feedback Seeking: A Race Perspective . In: Feedback at Work. 1st ed. edn. Edited by Steelman LA, Williams JR. Cham, Switzerland: Springer; 2019: 141-162. Brehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, Ivers N, Grimshaw JM: Practice Feedback Interventions: 15 Suggestions for Optimizing Effectiveness . Annals of internal medicine 2016, 164 (6):435-441. Ohly S, Sonnentag S, Niessen C, Zapf D: Diary studies in organizational research: An introduction and some practical recommendations . Journal of Personnel Psychology 2010, 9 (2):79-93. Additional Declarations No competing interests reported. Supplementary Files PrehospitalFeedbackDiaryStudy1AdditionalFile1V1.0.docx Additional file 1: Survey and diary study measures PrehospitalFeedbackDiaryStudy1AdditionalFile2V1.0.docx Additional file 2: In-depth data analysis plan, study hypotheses and theoretical models PrehospitalFeedbackDiaryStudy1AdditionalFile3V1.0.docx Additional file 3: Sensitivity analysis for the predicted likelihood of receiving feedback PrehospitalFeedbackDiaryStudy1AdditionalFile4V1.0.docx Additional file 4: Clustering dendogram and stability of the partitions PrehospitalFeedbackDiaryStudy1AdditionalFile5V1.0.docx Additional file 5: Results of the univariable and multivariable analyses (including basic and extended research models) PrehospitalFeedbackDiaryStudy1AdditionalFile6V1.0.docx Additional file 6: Sensitivity analyses of the predicted likelihood of feedback efficacy Cite Share Download PDF Status: Published Journal Publication published 13 Sep, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Revision requested 18 Jul, 2024 Reviews received at journal 17 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviews received at journal 01 Jul, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviews received at journal 19 May, 2024 Reviewers agreed at journal 09 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers invited by journal 24 Apr, 2024 Editor invited by journal 11 Mar, 2024 Submission checks completed at journal 11 Mar, 2024 Editor assigned by journal 11 Mar, 2024 First submitted to journal 04 Mar, 2024 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-4014306\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":278701106,\"identity\":\"63056b52-8e6c-487e-b4aa-cf558748e38f\",\"order_by\":0,\"name\":\"Caitlin Wilson\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJCCAw+ABD8z8wFm4rUkAAnJ9rYEiBY2YvSAtBj0nDEgTgt/A4/hgYQ/NnkGEjnfHhf8Ycjjl2/Ar0XiAI/BgcS2tGJzidztxjPbGIol2wjYYsDAu+FAYsPhxJ0zcrdJ8zYwJG44RoyWhD+HEzfcyHkmzfOHIXE/cVrYgFrOnGGT5mED2kLI+xKH+T+A/JI4s73NTJq3TSJxxrEE/Fr429uSP3z4Y5PYz8wMchiQ0XyAgDVoMS5BQPkoGAWjYBSMAqIAACi2RALDMRKcAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University of Leeds\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Caitlin\",\"middleName\":\"\",\"lastName\":\"Wilson\",\"suffix\":\"\"},{\"id\":278701107,\"identity\":\"565aa794-b564-48f7-99d7-bd597f0c1f51\",\"order_by\":1,\"name\":\"Luke Budworth\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yorkshire Quality and Safety Research Group, Bradford Institute for Health Research\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luke\",\"middleName\":\"\",\"lastName\":\"Budworth\",\"suffix\":\"\"},{\"id\":278701108,\"identity\":\"ad40c570-e1c0-40fd-9cee-7bf54fcaa9a0\",\"order_by\":2,\"name\":\"Gillian Janes\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Manchester Metropolitan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gillian\",\"middleName\":\"\",\"lastName\":\"Janes\",\"suffix\":\"\"},{\"id\":278701109,\"identity\":\"f9d55bc5-91a3-4c4f-bd4e-06ecd44d2b0f\",\"order_by\":3,\"name\":\"Rebecca Lawton\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Leeds\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rebecca\",\"middleName\":\"\",\"lastName\":\"Lawton\",\"suffix\":\"\"},{\"id\":278701110,\"identity\":\"4224a934-f8ad-424d-a865-33113830fdbc\",\"order_by\":4,\"name\":\"Jonathan Benn\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Leeds\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jonathan\",\"middleName\":\"\",\"lastName\":\"Benn\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-04 20:35:34\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4014306/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4014306/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12873-024-01082-y\",\"type\":\"published\",\"date\":\"2024-09-13T15:57:33+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":64619090,\"identity\":\"3bc83c37-140c-43cc-b210-865b80dd1b8d\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 16:11:21\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1819900,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/09d03ddd-57c1-4d78-b0a7-94898f1b2184.pdf\"},{\"id\":52629053,\"identity\":\"0307c717-8d10-40a0-b8c0-139f82940031\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30826,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 1: Survey and diary study measures\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile1V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/988c4212571011161cd13e86.docx\"},{\"id\":52629054,\"identity\":\"b83ee3eb-0c68-4357-afe1-b0cd2bc7b36d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":154627,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 2: In-depth data analysis plan, study hypotheses and theoretical models\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile2V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/c7939de05d99281ef78b6d9d.docx\"},{\"id\":52629057,\"identity\":\"4238b59d-53ef-4130-b378-4c772ee25e1e\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24517,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 3: Sensitivity analysis for the predicted likelihood of receiving feedback\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile3V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/88f22c7f9cb7d184fc815ceb.docx\"},{\"id\":52629052,\"identity\":\"fd0f2511-d81c-4da6-93d6-1d3d3f406bbd\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":67131,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 4: Clustering dendogram and stability of the partitions\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile4V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/1b5a1571d7e81d7e7dbdb700.docx\"},{\"id\":52629055,\"identity\":\"6dafbf1c-3255-434e-85ab-c502920f31ca\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":36338,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 5: Results of the univariable and multivariable analyses (including basic and extended research models)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile5V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/c2b475c3002fbb3bcaff6914.docx\"},{\"id\":52629056,\"identity\":\"fd755889-2643-46dd-b934-f9c4d40eddf2\",\"added_by\":\"auto\",\"created_at\":\"2024-03-13 18:47:55\",\"extension\":\"docx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":35711,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAdditional file 6: Sensitivity analyses of the predicted likelihood of feedback efficacy\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"PrehospitalFeedbackDiaryStudy1AdditionalFile6V1.0.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4014306/v1/fcc5dc8e4db28a200d028c93.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003ePrevalence, Predictors and Outcomes of Feedback for Ems Professionals: a Mixed-methods Diary Study\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eThe National Health Service (NHS) staff survey [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] consistently identifies Emergency Medical Services (EMS) professionals as the group with the highest work-related stress (55.7%), burnout (49.3%) and leaving intentions (42.9%) \\u0026ndash; with ~\\u0026thinsp;25% having applied for non-NHS jobs post COVID-19 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Receiving feedback on patient outcomes and personal performance may improve job support for EMS professionals and enhance staff wellbeing, job satisfaction and patient care [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAcross healthcare settings, including EMS, clinical performance feedback has been demonstrated to improve quality of care and professional development [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. However, recent reviews of existing literature [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] and current practice [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] recommend further research on the provision of patient outcome feedback and the impact of feedback on staff wellbeing in EMS.\\u003c/p\\u003e \\u003cp\\u003eEMS professionals could particularly benefit from feedback as their work environment is characterised by complexity, uncertainty and extreme stressors [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. EMS professionals work autonomously, making complex decisions including assessing and treating patients at home to avoid unnecessary hospital attendance and reduce demand on emergency departments [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Nevertheless, providing and accessing, EMS feedback on decision-making is difficult due to constraints such as a mobile workforce, disconnected digital technology [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e] and data sharing governance issues [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWhen feedback is provided for EMS professionals this is typically through formal initiatives, such as performance feedback during appraisals, patient outcome feedback from \\u0026ldquo;post-box\\u0026rdquo; schemes and patient-experience feedback through thank-you letters [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, qualitative research suggests that EMS professionals desire more and better feedback, especially concerning patient outcomes [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. When formal feedback initiatives are lacking, EMS professionals informally approach ED staff seeking feedback on patient outcomes [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. However, informal feedback is limited by patient confidentiality issues, information quality, verbal format and geographical barriers [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. While systematic reviews [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] and current practice [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] suggest formal feedback to EMS professionals positively affects patient care and clinical performance, it is unknown whether informal feedback or actively solicited feedback have similar outcomes.\\u003c/p\\u003e \\u003cp\\u003eIn the United States (US), it is estimated that feedback is provided to EMS professionals in just 24% of encounters [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] with 50\\u0026ndash;69% of paramedics self-reporting having received feedback in the previous month [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Particular recipient and contextual characteristics appear associated with increased feedback, including staff with higher level certifications, fewer years\\u0026rsquo; experience and working in busier or hospital-based organisations [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eLearning more about how the context and format of feedback impacts outcomes, as well as the mechanisms through which feedback influences outcomes, could be an important step in enhancing feedback effectiveness in EMS [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. In this vein, Clinical Performance Feedback Intervention Theory, which has good face validity in the prehospital setting [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], offers 42 hypotheses of when feedback is more effective e.g. when feeding back to staff with positive beliefs about feedback [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Feedback effectiveness is also predicted by the extent to which an organisation encourages, provides and uses feedback, i.e. the \\u0026lsquo;feedback environment\\u0026rsquo; [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], whereby a positive feedback environment predicts positive outcomes for individuals and organisations [\\u003cspan additionalcitationids=\\\"CR22 CR23\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite increasing research interest in prehospital feedback, no studies have explored the content and outcomes of prehospital feedback prospectively, or assessed feedback prevalence and predictors amongst EMS professionals in the United Kingdom. International studies have been limited by not drawing upon existing theory and potential recall bias [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. This study aimed to address these gaps by answering the following research questions:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eHow prevalent is feedback for UK EMS professionals and what types of feedback do they receive?\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eWhat individual and contextual factors predict EMS professionals receiving feedback in the previous 30 days?\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eWhat are the perceived outcomes of feedback for EMS professionals?\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eWhat predicts instances of feedback being perceived as improving outcomes?\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eStudy design\\u003c/h2\\u003e\\n\\u003cp\\u003eThis observational mixed-methods study consisted of a baseline survey followed by diary entries. Collecting diary entries in real time is known to reduce recall bias by collecting data at the level of feedback events and therefore not relying on generalised reflections of feedback provision over a period of time, whilst enabling analysis of within- and between-person variability [\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Diary entries were event-contingent and collected when a participant identified they had received feedback. Diary entries on desired feedback and a follow-up survey were part of the study but are not reported here.\\u003c/p\\u003e\\n\\u003cp\\u003eOpen-ended questions were used to contextualise and expand upon quantitative results [\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval\\u0026nbsp;was granted from the University of Leeds ethics committee (PSYC-406 04/01/2022) and the Health Research Authority (ID: 295645).\\u003c/p\\u003e\\n\\u003cp\\u003eSTROBE [\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e] and LEVEL recommendations [\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e] were followed.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eSetting and selection of participants\\u003c/h2\\u003e\\n\\u003cp\\u003eEligible participants were EMS clinicians (i.e. paramedics) and non-registered professionals (e.g. emergency medical technicians) delivering face-to-face patient care, employed by an NHS ambulance trust in the United Kingdom.\\u003c/p\\u003e\\n\\u003cp\\u003eAn opportunistic sample was recruited via social media and organisations\\u0026rsquo; internal communications. Informed consent was obtained in the baseline survey after providing study information. Access to the baseline survey was via an anonymous link, with individual diary study links issued to participants who provided their email address in their survey response. Participants completing all study elements were enrolled in a prize draw for three \\u0026pound;50 vouchers to aid recruitment and reduce drop-out.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eData collection\\u003c/h2\\u003e\\n\\u003cp\\u003eData was collected using Qualtrics (Qualtrics, Provo, UT) (March-August 2022). The survey and diary study measures were developed for this study (Additional file 1). They were piloted with three EMS professionals and refined based on their feedback.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eBaseline survey\\u003c/h2\\u003e\\n\\u003cp\\u003eThe baseline survey covered demographics, feedback frequency and feedback environment. Demographic questions included professional role, years of EMS experience, sex, age and ethnicity. The feedback frequency questions were adapted from a large-scale US EMS feedback survey [\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. They included items such as \\u0026lsquo;In the past 30 days, did you receive any feedback on the medical care you provided to a patient?\\u0026rsquo; scored on a dichotomous scale (\\u0026lsquo;yes/no\\u0026rsquo;). If answered positively, it was followed by \\u0026lsquo;How was this feedback provided? Verbal, by email, by text, written on paper, other\\u0026rsquo;.\\u003c/p\\u003e\\n\\u003cp\\u003eThe feedback environment measure was based upon the shortened Feedback Environment Scale (FES) [\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], which demonstrated excellent reliability for nurses (Cronbach\\u0026rsquo;s alpha 0.90) [\\u003cspan class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. The questions were adapted for the prehospital setting and reworded so as not to refer to a specific feedback source. Participants were asked to respond on a Likert-type scale ranging from 1\\u0026ndash;7 (strongly disagree-strongly agree) to statements such as \\u0026lsquo;I receive useful feedback at work\\u0026rsquo; and \\u0026lsquo;When I want feedback, this is readily available\\u0026rsquo;. Once respondents provided ratings for each of the 14 items, the scores were aggregated. A high score on the FES generally indicates a positive perception of the feedback environment [\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eDiary entries\\u003c/h2\\u003e\\n\\u003cp\\u003eImmediately after completing the baseline survey, participants were sent a link to access their diary which remained open until the end of the data collection period. When logging a feedback event, participants were asked a series of multiple choice and structured response questions informed by Clinical Performance Feedback Intervention Theory [\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], including, for example, \\u0026lsquo;How quickly after the incident was the feedback provided?\\u0026rsquo; and \\u0026lsquo;What effect do you think receiving this feedback had on your clinical practice/knowledge/confidence/sense of closure/job satisfaction/patient care/patient safety? Positive, negative or no effect\\u0026rsquo;.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eData analysis\\u003c/h2\\u003e\\n\\u003cp\\u003eQuantitative analyses was undertaken in R (Version 4.1.3, R Core Team) [\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] within RStudio [\\u003cspan class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e] and qualitative analyses in NVivo (Version 12 Plus, QSR International). The detailed multilevel data analysis plan [\\u003cspan class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e], study hypotheses and research models are described in Additional file 2.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eStudy size\\u003c/h2\\u003e\\n\\u003cp\\u003eAssuming 50/50 balanced binary predictors and normally distributed continuous predictors, 325 participants were required to detect any significant predictors of a medium-sized effect (i.e. Cohen\\u0026rsquo;s d\\u0026thinsp;=\\u0026thinsp;0.5) for prehospital feedback perceived as improving outcomes with 80% power, after adjustment for other variables [\\u003cspan class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The level-1 sample size was pre-specified by the research team as 10 diary entries was deemed an acceptable burden for each participant during stakeholder consultation. The power analysis was based on the basic research model, which included two level-2 predictors (role \\u0026ndash; binary, length in service \\u0026ndash; continuous) and two level-1 predictors (feedback content \\u0026ndash; categorical, solicited/unsolicited \\u0026ndash; binary).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eStatistical methods\\u003c/h2\\u003e\\n\\u003cp\\u003eData on the individual-level variables (role, length in service, FES score) were collected during the baseline survey. Data on the diary-level independent variables (feedback content, feedback-seeking behaviour, formal/informal, source, sign, format, lag-time) and dependent variable (feedback outcome) were collected for each diary entry. FES scale reliability was examined using Cronbach\\u0026rsquo;s alpha.\\u003c/p\\u003e\\n\\u003cp\\u003eTo describe feedback prevalence, descriptive statistics for baseline quantitative data were produced alongside content analysis of free-text qualitative responses.\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify predictors of receiving feedback in the last 30 days, baseline survey data were analysed using binary logistic regression (via \\u0026lsquo;lme4\\u0026rsquo;) [\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Univariable logistic regression assessed individual associations between each predictor (e.g. role) and the outcome (i.e. having received feedback in the previous 30 days). Multivariable logistic regression included all predictors simultaneously that formed part of the simple or extended research model.\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify predictors of feedback efficacy, data generated via feedback-received diary entries were analysed using multilevel logistic regression with random intercepts to account for multiple recorded feedback instances per participant. The variables of interest were chosen based on Clinical Performance Feedback Intervention Theory [\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] and qualitative exploratory studies of prehospital feedback [\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], for example feedback type, feedback-seeking behaviour and formal/informal. Continuous variables were grand-mean centred to improve the interpretation of the intercept values by shifting the focus to the effect of deviations from the overall average, rather than deviations from zero [\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eAkaike Information Criterion (AIC) [\\u003cspan class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] was used to compare models with the same outcome based on goodness-of-fit, whereby smaller AIC values indicate better fit. We did not adjust alpha for multiple comparisons due to deliberately favouring a higher Type I error rate relative to the potential for Type II error, as this was an exploratory study and it was deemed more harmful for future work to be conservative in potential leads [\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Analyses were conducted using complete cases, followed by sensitivity analyses dealing with missing data using the \\u0026lsquo;mice\\u0026rsquo; R package [\\u003cspan class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eTo categorise perceived outcomes of receiving feedback, hierarchical cluster analysis was performed on the baseline data (using \\u0026lsquo;ClustOfVar\\u0026rsquo; [\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]). Cluster analysis is an exploratory analysis that identifies structures within the data and visualises them in a dendrogram (tree diagram) with outcomes that co-occur most frequently placed on branches closer together [\\u003cspan class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Clusters were labelled by the research team using thematic classification informed by previous research [\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Free-text qualitative responses were analysed using content analysis.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCharacteristics of study participants\\u003c/h2\\u003e \\u003cp\\u003eTwo hundred and ninety-nine participants completed the baseline survey representing 13 of the 14 UK ambulance trusts (median 19, range 4\\u0026ndash;88 participants per trust). Of these, 105 completed 538 feedback-received diary entries (range 1\\u0026ndash;16, median 4).\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e summarises participants\\u0026rsquo; baseline characteristics. Ethnicity was collapsed into a binary variable (white m\\u0026thinsp;=\\u0026thinsp;290, minoritised ethnic group n\\u0026thinsp;=\\u0026thinsp;8) to avoid identifying participants. Inferential statistics did not indicate that participants\\u0026rsquo; characteristics significantly differed between the baseline survey and diary entry stages. Comparison with national data for UK ambulance services [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e] using chi-square tests at 0.05 significance level indicated that our study sample was representative in terms of ethnicity (p\\u0026thinsp;=\\u0026thinsp;0.771), sex (p\\u0026thinsp;=\\u0026thinsp;0.124) and age (p\\u0026thinsp;=\\u0026thinsp;0.886).\\u003c/p\\u003e \\u003cp\\u003eThe FES was found to have excellent internal consistency (alpha\\u0026thinsp;=\\u0026thinsp;0.85 [95% CI 0.81 to 0.88]).\\u003c/p\\u003e \\u003cp\\u003eOf 299 baseline surveys, 77 (25.5%) were incomplete. Missing values varied from 0.3\\u0026ndash;25.1%.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCharacteristics of study participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBaseline survey\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDiary entries\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNumber of participants\\u003c/b\\u003e, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e299\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e105\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRole\\u003c/b\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmergency Medical Technician\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59 (19.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (15.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParamedic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e239 (79.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89 (84.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge in years, \\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003emedian (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003cp\\u003e(29.0\\u0026ndash;45.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003cp\\u003e(30.5\\u0026ndash;45.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e120 (40.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39 (37.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e177 (59.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66 (62.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNot stated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (0.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnicity\\u003c/b\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinoritised ethnic group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8 (2.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (1.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e290 (97.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103 (98.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNot stated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (0.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eYears of work experience\\u003c/b\\u003e, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003cp\\u003e(3.7\\u0026ndash;13.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003cp\\u003e(4.5\\u0026ndash;14.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFES score\\u003c/b\\u003e, mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e53.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePresence of formal feedback initiative\\u003c/b\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68 (22.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26 (24.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e231 (77.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79 (75.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFeedback prevalence and types\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e describes the characteristics of feedback prevalence from the baseline data and diary entries.\\u003c/p\\u003e \\u003cp\\u003eOf the 299 participants completing the baseline survey, 215 (71.9%) indicated that they had received feedback in the last 30 days, with patient outcome feedback being the most frequently received (n\\u0026thinsp;=\\u0026thinsp;149, 42.8%). Feedback was predominantly provided in verbal format (n\\u0026thinsp;=\\u0026thinsp;157, 73.0%) and was informal (n\\u0026thinsp;=\\u0026thinsp;189, 80.4%). Diary entries indicated that the median perceived usefulness of feedback was 6 (IQR 5\\u0026ndash;7).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCharacteristics of feedback prevalence at baseline and during diary study\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBaseline feedback received (N\\u0026thinsp;=\\u0026thinsp;215), n(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDiary entries feedback received (N\\u0026thinsp;=\\u0026thinsp;538), n(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eType\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatient outcome feedback\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e149 (42.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e226 (42.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatient experience feedback\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e88 (25.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e108 (20.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eClinical performance feedback\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e111 (31.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e201 (37.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIncident-prompted feedback\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (0.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePost-event debriefing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSource\\u003c/b\\u003e \\u003cem\\u003e(multiple selections possible)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-ambulance healthcare professionals\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e114 (33.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e202 (37.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEMS professionals or managers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132 (39.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e180 (33.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatients/relatives\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e85 (25.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e147 (27.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eElectronic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41 (19.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (16.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVerbal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e157 (73.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e414 (77.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWritten\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16 (7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 (5.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (0.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLag time\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eImmediate or within 1 day\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e130 (45.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e370 (68.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u0026ndash;3 days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e53 (18.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (8.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u0026ndash;7 days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27 (9.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 (7.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u0026ndash;14 days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28 (9.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (4.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMore than 14 days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e46 (16.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e58 (10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFeedback-seeking behaviour\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnsolicited\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e143 (53.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e335 (62.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSolicited\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e124 (46.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e203 (37.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFormal/informal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e46 (19.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87 (16.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInformal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e189 (80.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e451 (83.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSign\\u003c/b\\u003e (i.e. nature or direction of feedback)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e383 (71.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutral\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e91 (17.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (3.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMixed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (8.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUsefulness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1 \\u0026ndash; Not useful at all\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (0.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (3.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (4.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e70 (13.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e120 (22.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e155 (28.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7 \\u0026ndash; Extremely useful\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot collected\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e178 (33.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePredicted likelihood of receiving feedback\\u003c/h2\\u003e \\u003cp\\u003eThe likelihood of receiving feedback in the past 30 days was higher for those with higher FES scores (aOR 1.07 [1.04, 1.10]), meaning that each one-point increase in Feedback Environment Scale increased the odds of receiving feedback by 7% (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Participants in paramedic roles had three times the estimated odds of receiving feedback than non-registered EMS professionals (aOR 3.04 [1.14, 8.00]). Those of white ethnicity had five times the estimated odds of receiving feedback compared with minoritised ethnic group participants (aOR 5.68 [1.01, 29.73]); although, the wide confidence interval indicates a high level of uncertainty in this estimate. The sensitivity analysis (Additional file 3) indicated that when missing data was imputed, ethnicity did not predict the likelihood of receiving feedback (aOR 3.34 [0.71, 15.71]).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFactors associated with receiving feedback in the past 30 days\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnivariable\\u003c/p\\u003e \\u003cp\\u003eOR (95% CI), p-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMultivariable\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI), p-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFES\\u003c/b\\u003e \\u003cem\\u003e(continuous)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.06 (1.04, 1.09)*, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.07 (1.04, 1.10), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRole\\u003c/b\\u003e \\u003cem\\u003e(binary)\\u003c/em\\u003e (ref\\u0026thinsp;=\\u0026thinsp;Emergency Medical Technician)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParamedic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.65 (0.70, 3.65), p\\u0026thinsp;=\\u0026thinsp;0.231\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.04 (1.14, 8.00), p\\u0026thinsp;=\\u0026thinsp;0.024*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e \\u003cem\\u003e(binary)\\u003c/em\\u003e (ref\\u0026thinsp;=\\u0026thinsp;Female)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.12 (0.56, 2.18), p\\u0026thinsp;=\\u0026thinsp;0.750\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.14 (0.54, 2.39), p\\u0026thinsp;=\\u0026thinsp;0.723\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnicity\\u003c/b\\u003e \\u003cem\\u003e(binary)\\u003c/em\\u003e (ref\\u0026thinsp;=\\u0026thinsp;Minoritised ethnic group)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.07 (0.59, 14.44), p\\u0026thinsp;=\\u0026thinsp;0.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.68 (1.01, 29.73), p\\u0026thinsp;=\\u0026thinsp;0.037*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eYears of work experience\\u003c/b\\u003e \\u003cem\\u003e(continuous)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.97 (0.93, 1.01), p\\u0026thinsp;=\\u0026thinsp;0.107\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.92, 1.04), p\\u0026thinsp;=\\u0026thinsp;0.523\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e \\u003cem\\u003e(continuous)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.97 (0.94, 1.00), p\\u0026thinsp;=\\u0026thinsp;0.069\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.94, 1.03), p\\u0026thinsp;=\\u0026thinsp;0.411\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eExistence of feedback initiative\\u003c/b\\u003e \\u003cem\\u003e(binary)\\u003c/em\\u003e (ref\\u0026thinsp;=\\u0026thinsp;no)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.25 (0.58, 2.96), p\\u0026thinsp;=\\u0026thinsp;0.584\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.07 (0.45, 2.74), p\\u0026thinsp;=\\u0026thinsp;0.884\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOutcomes of feedback\\u003c/h2\\u003e \\u003cp\\u003eFeedback outcomes were categorised into three clusters following a visual inspection of the dendrogram from the hierarchical cluster analysis and stability of the partitions (Additional file 4). Cluster 1 (\\u0026lsquo;\\u003cem\\u003eprofessional development\\u003c/em\\u003e\\u0026rsquo;) encompassed clinical practice and knowledge, Cluster 2 (\\u0026lsquo;\\u003cem\\u003epersonal wellbeing\\u0026rsquo;\\u003c/em\\u003e) encompassed closure, confidence and job satisfaction, and Cluster 3 (\\u0026lsquo;\\u003cem\\u003eservice outcomes\\u0026rsquo;\\u003c/em\\u003e) encompassed patient care and patient safety.\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e describes the proportion of positive, negative, mixed and no impact within each feedback outcome cluster.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePerceived impact within each feedback outcome cluster\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProfessional development\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePersonal wellbeing\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eService outcomes\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePositive impact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e434 (80.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e475 (88.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e405 (75.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo impact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95 (17.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (2.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNegative impact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33 (6.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 (2.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMixed impact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (2.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eOverall, feedback was reported to have a positive impact. This was even more apparent for feedback that had personal wellbeing implications (88.3%). Free-text comments described positive impacts as making participants feel \\u0026ldquo;\\u003cem\\u003egood\\u003c/em\\u003e\\u0026rdquo; or \\u0026ldquo;\\u003cem\\u003egreat\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;126), \\u0026ldquo;\\u003cem\\u003esatisfied that they had done a good job\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;91), \\u0026ldquo;\\u003cem\\u003ehappy\\u003c/em\\u003e\\u0026rdquo; or \\u0026ldquo;\\u003cem\\u003epleased\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;88), \\u0026ldquo;\\u003cem\\u003emotivated\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;70), \\u0026ldquo;\\u003cem\\u003eappreciated\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;53) and \\u0026ldquo;\\u003cem\\u003ethankful\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;15). However, feedback in this category also had the highest percentage of negative impacts (6.1% vs 1.5 and 2.2%) and very rarely had no impact at all (2.8%).\\u003c/p\\u003e \\u003cp\\u003eNegative impacts were further described by participants as being \\u0026ldquo;\\u003cem\\u003esad\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;18), \\u0026ldquo;\\u003cem\\u003eannoyed\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;22) and feeling \\u0026ldquo;\\u003cem\\u003edefensive\\u003c/em\\u003e\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;18), for example when the \\u0026ldquo;\\u003cem\\u003e[prehospital] patient presentation was completely different to [the hospital] diagnosis\\u003c/em\\u003e\\u0026rdquo; (PID 118). One participant described that they were \\u0026ldquo;\\u003cem\\u003enervous, questioned [my] practice, and concerned that I may not have done [the] job properly\\u003c/em\\u003e\\u0026rdquo; (PID 177). Another participant recalled feeling \\u0026ldquo;\\u003cem\\u003edeflated, angry, and disappointed\\u003c/em\\u003e\\u0026rdquo; (PID 74). The 33 feedback events resulting in negative affective responses were reported by 25 participants, who had lower FES scores and received punitive feedback that was predominantly negative, unsolicited and provided by EMS professionals.\\u003c/p\\u003e \\u003cp\\u003eNo impact was most commonly recorded for service outcomes. This was echoed in the baseline survey, in which only 1 of the 299 participants (0.3%) indicated that the feedback they had received in the past 30 days resulted in a change to professional practice across the organisation. According to the survey data, changes at a team level were also rare (n\\u0026thinsp;=\\u0026thinsp;5, 1.7%), with the two free-text responses indicating it changed how the team \\u0026ldquo;\\u003cem\\u003eapproach[ed] certain cases\\u003c/em\\u003e\\u0026rdquo; (PID 172) and \\u0026ldquo;\\u003cem\\u003enon-conveyances rates\\u003c/em\\u003e\\u0026rdquo; (PID 103). In contrast, nearly 1 in every 5 participants (n\\u0026thinsp;=\\u0026thinsp;54, 18.1%) reported changes at an individual level. Examples included developing \\u0026ldquo;\\u003cem\\u003ea more structured approach to history taking\\u003c/em\\u003e\\u0026rdquo; (PID 42, 99, 237), improving documentation (PID 199, 261, 107, 184, 285) and having a greater awareness of how unusual conditions present (PID 71, 156, 187). Eight participants described that feedback had changed their clinical decision-making and that they would subsequently manage similar cases differently (PID 54, 93, 128, 193, 197, 198, 235, 258).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePredicted likelihood of feedback efficacy\\u003c/h2\\u003e \\u003cp\\u003eAdditional file 5 summarises the results of the univariable and multivariable multilevel analyses identifying predictors of feedback efficacy. Sensitivity analyses (Additional file 6) indicated that missing data had some effect in the univariable analyses but little effect in the multivariable multilevel analyses. The ICC\\u003csub\\u003eProfessional\\u003c/sub\\u003e=0.25, ICC\\u003csub\\u003ePersonal\\u003c/sub\\u003e=0.19 and ICC\\u003csub\\u003eService\\u003c/sub\\u003e=0.24 indicated that between 19% \\u0026minus;\\u0026thinsp;25% of the variance was explained by between-participant differences. This means that a moderate amount of the variablity in feedback having a positive impact on professional development, personal wellbeing and service outcomes, was explained at a participant level, rather than at the level of individual feedback events.\\u003c/p\\u003e \\u003cp\\u003eComparing the AICs for the basic and extended research model suggested that the extended research model was the best fit for all three outcome clusters. The extended research model indicated that feedback-seeking behaviour and FES were statistically significant predictors of feedback efficacy. Solicited feedback was more likely to improve professional development (aOR 3.35 [1.68, 6.69]) and personal wellbeing (aOR 2.58 [1.19, 5.56]) than unsolicited feedback. A one-point increase in FES led to a predicted 4% increase in the odds of feedback positively affecting personal wellbeing (aOR 1.04 [1.01, 1.07]) and a 3% increase for service outcomes (aOR 1.03 [1.00, 1.06]).\\u003c/p\\u003e \\u003cp\\u003eResults from the multilevel univariable analyses suggested that improvements in professional development were less likely with patient-experience feedback than patient outcome or clinical performance feedback (OR 0.38 [0.20, 0.75]), but this was not replicated in the sensitivity analysis indicating that missing data had an effect on this predictor.\\u003c/p\\u003e \\u003cp\\u003eFeedback from healthcare professionals outside of the ambulance service had almost five times the estimated odds of positively affecting professional development than feedback from other sources (OR 4.93 [2.93, 8.29]). Negative feedback was less likely to improve professional development than positive feedback (OR 0.18 [0.04, 0.73]).\\u003c/p\\u003e \\u003cp\\u003eSimilarly, negative feedback was much less likely to improve personal wellbeing (OR 0.12 [0.02, 0.56]), whilst positive feedback had over thirty times the estimated odds of positively affecting personal wellbeing (OR 31.0 [11.4, 84.3]). Improvements to personal wellbeing was most likely if feedback was provided by patients or relatives than other sources (OR 3.36 [1.45, 7.80]).\\u003c/p\\u003e \\u003cp\\u003eThe univariable analyses for service outcomes did not indicate any statistically significant results.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eIn total, 215 (71.9%) participants indicated that they had received feedback in the last 30 days with patient outcome feedback most received (n\\u0026thinsp;=\\u0026thinsp;149, 42.8%). Significant predictors for receiving feedback were a paramedic role and a workplace with a positive feedback-seeking culture. Participants reported that feedback affected personal wellbeing (closure, confidence, job satisfaction), professional development (clinical practice, knowledge) and service outcomes (patient care, patient safety). Solicited or positive feedback was more likely to positively affect personal and professional development than unsolicited or negative feedback.\\u003c/p\\u003e \\u003cp\\u003eCompared to US studies, our participants reported a slightly higher prevalence of receiving feedback in the past 30 days: 71.9% compared to 50.0% [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] and 69.4% [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. This could be because our study provided clearer specification of feedback through definitions provided to participants.\\u003c/p\\u003e \\u003cp\\u003eConsistent with other studies, feedback was mostly received in verbal format (73.0%) and provided by a mixture of EMS professionals (39.3%), non-ambulance healthcare professionals (33.9%) and patients or relatives (25.3%) [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Patient outcome feedback was the type most frequently received by our participants (42.8%), which differs from the largest US study on this topic in which receipt of clinical performance feedback dominated [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn contrast to previous studies of prehospital feedback [\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], years of experience were not a significant predictor of receiving feedback in our study. However, we did identify several novel predictors of receiving feedback, such as paramedic role and a workplace with a positive feedback culture as indicated by high FES scores. Paramedics may receive more feedback compared with non-registered EMS professionals because they take the lead on more acute cases and are therefore in a better position to actively seek feedback, as indicated by 38.6% (n\\u0026thinsp;=\\u0026thinsp;180) of feedback for paramedics being solicited compared with only 31.9% (n\\u0026thinsp;=\\u0026thinsp;23) for emergency medical technicians. It may also be that paramedics have become used to receiving enhanced feedback during undergraduate training or the newly qualified paramedic period and are therefore continuing to seek enhanced feedback provision [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. The broader feedback literature offers theoretical support regarding feedback exchanges being affected by social categories such as race, gender, age and sexual orientation, in that staff with minority characteristics are less likely to actively seek feedback [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Further understanding how personal characteristics influence EMS feedback interactions is vital to promote equity and inclusion within feedback theory and practice.\\u003c/p\\u003e \\u003cp\\u003eOur analysis indicates that solicited feedback was more likely to improve professional development and personal wellbeing than unsolicited feedback. This may be due to solicited feedback being timelier, more relevant and originating from a more credible source as the recipient has some control over whom they approach, compared with unsolicited feedback. Overall this probably reflects the limitations of the existing prehospital feedback provision in regards to timeliness, relevance and credibility, rather than solicited feedback being an ultimate desirable goal [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe positive effects of prehospital feedback on quality of care and professional development were synthesised in a recent systematic review [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], but EMS professionals in our study also perceived that feedback positively affects personal outcomes such as closure (68.8%), confidence (83.1%) and job satisfaction (81.8%). This confirms suggestions from qualitative and survey studies that feedback for EMS professionals can support staff wellbeing and job satisfaction [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Feedback made our study participants feel they were \\u0026ldquo;\\u003cem\\u003epart of a patient\\u0026rsquo;s journey\\u003c/em\\u003e\\u0026rdquo; (PID 196) and as if they \\u0026ldquo;\\u003cem\\u003ehad made a difference\\u003c/em\\u003e\\u0026rdquo; (PID 113, PID 143).\\u003c/p\\u003e \\u003cp\\u003eOur study also highlights the importance of feedback delivery as demonstrated by negative impacts not only being related to a negative patient outcome, but also by feedback being perceived as \\u0026ldquo;\\u003cem\\u003egood, but also patronising a little\\u003c/em\\u003e\\u0026rdquo; (PID 55), participants feeling \\u0026ldquo;\\u003cem\\u003euncomfortable\\u003c/em\\u003e\\u0026rdquo; due to the way feedback was delivered (PID 73) and feedback being perceived as \\u0026ldquo;\\u003cem\\u003enot genuine\\u003c/em\\u003e\\u0026rdquo; (PID 215) or having \\u0026ldquo;\\u003cem\\u003eno sincerity\\u003c/em\\u003e\\u0026rdquo; (PID 215). Credibility of the feedback source and content has been identified as influencing feedback effectiveness in the broader audit and feedback literature [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e] [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplications for research and practice\\u003c/h2\\u003e \\u003cp\\u003eFurther research should include developing theory-informed measures to evaluate how prehospital feedback initiatives impact professional practice, personal wellbeing and service outcomes. Observational studies within EMS should be conducted to deepen our understanding of solicited and unsolicited feedback, the utility of negative feedback and the influence of personal characteristics on EMS feedback interactions and engagement. A particular area in need of further research are minoritised ethnic EMS professionals. Further research should also focus on what feedback EMS professionals want to receive.\\u003c/p\\u003e \\u003cp\\u003eChange in clinical practice should focus on designing and robustly evaluating feedback provision for EMS professionals. All EMS professionals should be enabled to make better use of the feedback they have access to. Non-registered EMS professionals should be supported to actively seek feedback to address the current feedback inequity, which places them at a disadvantage when it comes to development of professional competency and performance. Care should be taken in feeding back service level outcomes to frontline EMS professionals to ensure that the feedback is relevant and actionable at their level.\\u003c/p\\u003e \\u003cp\\u003eTailoring feedback interventions to support personal wellbeing is most likely to be perceived by EMS professionals to have positive impacts than those targeting professional development or service outcomes. The benefits of feedback for staff wellbeing should be formally recognised by ambulance services given the potential to mitigate workforce challenges, such as burnout, retention and recruitment. Feedback targeting personal wellbeing may also do harm and organisations should adequately support EMS professionals when receiving feedback.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and Limitations\\u003c/h2\\u003e \\u003cp\\u003eThis was the first study to assess feedback prevalence within the UK EMS population and to explore the associated contextual factors and outcomes. This study was limited by the high drop-out rate (n\\u0026thinsp;=\\u0026thinsp;299 participants at baseline, n\\u0026thinsp;=\\u0026thinsp;105 logging diary entries), though this is typical of diary studies generally [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. To combat high dropout in future diary studies, researchers could offer greater incentives or further reduce survey length. However, using diary methods was a novel way to assess feedback prevalence that reduced recall bias and provided reliable within-person data. Testing for differences between the prospective diary entries and retrospective baseline data to quantify recall bias indicated significantly shorter lag times (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and a higher proportion of unsolicited feedback (p\\u0026thinsp;=\\u0026thinsp;0.018) for the prospectively collected data, suggesting that retrospective data collection may not be reliable for feedback in EMS.\\u003c/p\\u003e \\u003cp\\u003eDespite data collection taking place during the early post-pandemic period when the backlog of health needs were emerging, the large number of NHS staff that participated and feedback events that were reported, indicate an appetite for feedback research from EMS professionals. However, this study was unable to recruit to target. Challenges related to the demanding schedules and limited availability for research participation of the target NHS staff group, combined with reliance on voluntary participation, are likely to have contributed to the relatively low response rate. Future research should explore alternative recruitment strategies to enhance participation rates within this professional context.\\u003c/p\\u003e \\u003cp\\u003eComparison with national data for UK ambulance services [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e] indicated that our study sample was representative of UK EMS but it remains unclear to what extent these findings might be replicated in the health systems of other countries. We acknowledge that collapsing our ethnicity variable into binary categories limits our conclusions regarding specific ethnic minority groups. The divergence between the complete case analysis and the multiple imputation sensitivity analysis regarding whether ethnicity predicted the likelihood of receiving feedback suggests this predictor may not be very robust. However, as feedback is mostly positive, this is a potential inequality and needs further investigation. Future studies should specifically target minority group participation, particularly as the literature suggests that social identity and race influence feedback-seeking behaviour [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"CONCLUSIONS\",\"content\":\"\\u003cp\\u003eIn conclusion, our study provides valuable insights into the prevalence, predictors and outcomes of feedback provision within the UK EMS context. Our findings underscore the importance of feedback in enhancing not only clinical practice and service outcomes but also personal wellbeing and job satisfaction among EMS professionals. However, the delivery of feedback emerged as a critical factor influencing its effectiveness, highlighting the need for attention to credibility and sensitivity in feedback delivery. Addressing feedback inequities, particularly among non-registered EMS professionals and minoritised groups, is crucial for promoting workforce development and ensuring equitable access to development opportunities. Overall, this study suggests that EMS workplaces need to develop a culture that encourages feedback-seeking by ensuring high-quality positive and negative feedback is readily available and provided by a credible source to strengthen the impact of feedback for EMS professionals on clinical decision-making and staff wellbeing.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eAIC - Akaike Information Criterion\\u003c/p\\u003e\\n\\u003cp\\u003eED \\u0026ndash; Emergency Department\\u003c/p\\u003e\\n\\u003cp\\u003eEMS \\u0026ndash; Emergency Medical Services\\u003c/p\\u003e\\n\\u003cp\\u003eFES \\u0026ndash; Feedback Environment Scale\\u003c/p\\u003e\\n\\u003cp\\u003eNHS \\u0026ndash; National Health Service\\u003c/p\\u003e\\n\\u003cp\\u003eOR \\u0026ndash; Odds Ratio\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u0026nbsp;\\u003c/strong\\u003eThe study was carried out in accordance with the UK Policy Framework for Health and Social Care Research (Health Research Authority, 2017) and was approved by the Health Research Authority (IRAS project ID 295645) and the University of Leeds ethics committee (PSYC-406 04/01/2022). Informed consent was obtained in the baseline survey after providing participants with study information.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e: Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e: The datasets generated and analysed during the current study are not publically available as sharing the raw data would violate the agreement to which participants consented; however, the datasets are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e: The authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e: This research was funded by the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions:\\u0026nbsp;\\u003c/strong\\u003eCW conceived the study, developed the study protocol, obtained relevant ethics and governance approvals, collected the data, analysed the data and drafted the manuscript under supervision from GJ, RL and JB. LB provided guidance on the statistical analysis plan, sample size calculation and data analysis. CW drafted the article and all authors contributed substantially to its revision and approved the final version. CW takes responsibility for the paper as a whole.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e: The authors would like to thank the study participants for taking the time to complete the survey and log diary entries, as well as the research departments of participating ambulance trusts for their support with advertisement and recruitment. Thank you to Professor Helen Snooks, University of Swansea, and Professor Graham Law, University of Lincoln, for peer-reviewing the study protocol.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eNHS England: \\u003cstrong\\u003eNHS Staff Survey 2022 - National results briefing\\u003c/strong\\u003e. In\\u003cem\\u003e.\\u003c/em\\u003e: NHS; 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eWeyman A, Glendinning R, O\\u0026rsquo;Hara R, Coster J, Roy D, Nolan P: \\u003cstrong\\u003eShould I stay or should I go? NHS staff retention in the post COVID-19 world: Challenges and prospects - IRR Report\\u003c/strong\\u003e. In\\u003cem\\u003e.\\u003c/em\\u003e: University of Bath; 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eWilson C, Howell A-M, Janes G, Benn J: \\u003cstrong\\u003eThe role of feedback in emergency ambulance services: a qualitative interview study\\u003c/strong\\u003e. \\u003cem\\u003eBMC Health Services Research \\u003c/em\\u003e2022, \\u003cstrong\\u003e22\\u003c/strong\\u003e:296.\\u003c/li\\u003e\\n\\u003cli\\u003eEaton-Williams P, Mold F, Magnusson C: \\u003cstrong\\u003eExploring paramedic perceptions of feedback using a phenomenological approach\\u003c/strong\\u003e. \\u003cem\\u003eBritish Paramedic Journal \\u003c/em\\u003e2020, \\u003cstrong\\u003e5\\u003c/strong\\u003e(1):7-14.\\u003c/li\\u003e\\n\\u003cli\\u003eIvers N, Jamtvedt G, Flottorp S, Young JM, Odgaard‐Jensen J, French SD, O\\u0026apos;Brien MA, Johansen M, Grimshaw J, Oxman AD: \\u003cstrong\\u003eAudit and feedback: effects on professional practice and healthcare outcomes\\u003c/strong\\u003e. \\u003cem\\u003eCochrane Database of Systematic Reviews \\u003c/em\\u003e2012(6).\\u003c/li\\u003e\\n\\u003cli\\u003eWilson C, Janes G, Lawton R, Benn J: \\u003cstrong\\u003eThe types and effects of feedback for emergency ambulance staff: a systematic mixed studies review and meta-analysis\\u003c/strong\\u003e. \\u003cem\\u003eBMJ quality \\u0026amp; safety \\u003c/em\\u003e2023, \\u003cstrong\\u003e32\\u003c/strong\\u003e:573-588.\\u003c/li\\u003e\\n\\u003cli\\u003eWilson C, Janes G, Lawton R, Benn J: \\u003cstrong\\u003eFeedback for Emergency Ambulance Staff: A National Review of Current Practice Informed by Realist Evaluation Methodology\\u003c/strong\\u003e. \\u003cem\\u003eHealthcare \\u003c/em\\u003e2023, \\u003cstrong\\u003e11\\u003c/strong\\u003e(16).\\u003c/li\\u003e\\n\\u003cli\\u003eFisher JD, Freeman K, Clarke A, Spurgeon P, Smyth M, Perkins GD, Sujan MA, Cooke MW: \\u003cstrong\\u003eHealth Services and Delivery Research\\u003c/strong\\u003e. In: \\u003cem\\u003ePatient safety in ambulance services: a scoping review.\\u003c/em\\u003e edn. Southampton (UK): NIHR Journals Library; 2015.\\u003c/li\\u003e\\n\\u003cli\\u003eLawn S, Roberts L, Willis E, Couzner L, Mohammadi L, Goble E: \\u003cstrong\\u003eThe effects of emergency medical service work on the psychological, physical, and social well-being of ambulance personnel: a systematic review of qualitative research\\u003c/strong\\u003e. \\u003cem\\u003eBMC Psychiatry \\u003c/em\\u003e2020, \\u003cstrong\\u003e20\\u003c/strong\\u003e(1):348.\\u003c/li\\u003e\\n\\u003cli\\u003ePaulin J, Kurola J, Koivisto M, Iirola T: \\u003cstrong\\u003eEMS non-conveyance: A safe practice to decrease ED crowding or a threat to patient safety?\\u003c/strong\\u003e \\u003cem\\u003eBMC Emerg Med \\u003c/em\\u003e2021, \\u003cstrong\\u003e21\\u003c/strong\\u003e(1):115.\\u003c/li\\u003e\\n\\u003cli\\u003eBlodgett JM, Robertson DJ, Pennington E, Ratcliffe D, Rockwood K: \\u003cstrong\\u003eAlternatives to direct emergency department conveyance of ambulance patients: a scoping review of the evidence\\u003c/strong\\u003e. \\u003cem\\u003eScand J Trauma Resusc Emerg Med \\u003c/em\\u003e2021, \\u003cstrong\\u003e29\\u003c/strong\\u003e(1):4.\\u003c/li\\u003e\\n\\u003cli\\u003ePorter A, Badshah A, Black S, Fitzpatrick D, Harris-Mayes R, Islam S, Jones M, Kingston M, LaFlamme-Williams Y, Mason S\\u003cem\\u003e et al\\u003c/em\\u003e: \\u003cstrong\\u003eElectronic health records in ambulances: the ERA multiple-methods study\\u003c/strong\\u003e. \\u003cem\\u003eHealth Serv Deliv Res \\u003c/em\\u003e2020, \\u003cstrong\\u003e8\\u003c/strong\\u003e(10).\\u003c/li\\u003e\\n\\u003cli\\u003eMorrison L, Cassidy L, Welsford M, Chan TM: \\u003cstrong\\u003eClinical Performance Feedback to Paramedics: What They Receive and What They Need\\u003c/strong\\u003e. \\u003cem\\u003eAEM Education and Training \\u003c/em\\u003e2017, \\u003cstrong\\u003e1\\u003c/strong\\u003e(2):87-97.\\u003c/li\\u003e\\n\\u003cli\\u003eMock EF, Wrenn KD, Wright SW, Eustis TC, Slovis CM: \\u003cstrong\\u003eFeedback to emergency medical services providers: the good, the bad, and the ignored\\u003c/strong\\u003e. \\u003cem\\u003ePrehospital and disaster medicine \\u003c/em\\u003e1997, \\u003cstrong\\u003e12\\u003c/strong\\u003e(2):145-148.\\u003c/li\\u003e\\n\\u003cli\\u003eCash RE, Crowe RP, Rodriguez SA, Panchal AR: \\u003cstrong\\u003eDisparities in feedback provision to emergency medical services professionals\\u003c/strong\\u003e. \\u003cem\\u003ePrehospital Emergency Care \\u003c/em\\u003e2017, \\u003cstrong\\u003e21\\u003c/strong\\u003e(6):773-781.\\u003c/li\\u003e\\n\\u003cli\\u003eMcGuire SS, Luke A, Klassen AB, Myers LA, Mullan AF, Sztajnkrycer MD: \\u003cstrong\\u003eIt\\u0026rsquo;s Time to Talk to Prehospital Providers: Feedback Disparities among Ground-Based Emergency Medical Services Providers and its Impact on Job Satisfaction\\u003c/strong\\u003e. \\u003cem\\u003ePrehospital and disaster medicine \\u003c/em\\u003e2021, \\u003cstrong\\u003e36\\u003c/strong\\u003e(4):486-494.\\u003c/li\\u003e\\n\\u003cli\\u003eHysong SJ, Kell HJ, Petersen LA, Campbell BA, Trautner BW: \\u003cstrong\\u003eTheory-based and evidence-based design of audit and feedback programmes: examples from two clinical intervention studies\\u003c/strong\\u003e. \\u003cem\\u003eBMJ quality \\u0026amp; safety \\u003c/em\\u003e2017, \\u003cstrong\\u003e26\\u003c/strong\\u003e(4):323.\\u003c/li\\u003e\\n\\u003cli\\u003eBrown B, Gude WT, Blakeman T, van der Veer SN, Ivers N, Francis JJ, Lorencatto F, Presseau J, Peek N, Daker-White G: \\u003cstrong\\u003eClinical 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\\u003c/strong\\u003e. \\u003cem\\u003eImplementation Science \\u003c/em\\u003e2019, \\u003cstrong\\u003e14\\u003c/strong\\u003e(1):40.\\u003c/li\\u003e\\n\\u003cli\\u003eRife GL: \\u003cstrong\\u003eThe influence of feedback orientation and feedback environment on clinician processing of feedback from client outcome measures\\u003c/strong\\u003e. University of Akron; 2016.\\u003c/li\\u003e\\n\\u003cli\\u003eLondon M, Smither JW: \\u003cstrong\\u003eFeedback orientation, feedback culture, and the longitudinal performance management process\\u003c/strong\\u003e. \\u003cem\\u003eHuman Resource Management Review \\u003c/em\\u003e2002, \\u003cstrong\\u003e12\\u003c/strong\\u003e(1):81-100.\\u003c/li\\u003e\\n\\u003cli\\u003eNorris-Watts C, Levy PE: \\u003cstrong\\u003eThe mediating role of affective commitment in the relation of the feedback environment to work outcomes\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Vocational Behavior \\u003c/em\\u003e2004, \\u003cstrong\\u003e65\\u003c/strong\\u003e(3):351-365.\\u003c/li\\u003e\\n\\u003cli\\u003eRosen CC, Levy PE, Hall RJ: \\u003cstrong\\u003ePlacing perceptions of politics in the context of the feedback environment, employee attitudes, and job performance\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Applied Psychology \\u003c/em\\u003e2006, \\u003cstrong\\u003e91\\u003c/strong\\u003e(1):211-220.\\u003c/li\\u003e\\n\\u003cli\\u003eSparr JL, Sonnentag S: \\u003cstrong\\u003eFeedback environment and well-being at work: The mediating role of personal control and feelings of helplessness\\u003c/strong\\u003e. \\u003cem\\u003eEuropean Journal of Work and Organizational Psychology \\u003c/em\\u003e2008, \\u003cstrong\\u003e17\\u003c/strong\\u003e(3):388-412.\\u003c/li\\u003e\\n\\u003cli\\u003eWhitaker BG, Dahling JJ, Levy P: \\u003cstrong\\u003eThe Development of a Feedback Environment and Role Clarity Model of Job Performance\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Management \\u003c/em\\u003e2007, \\u003cstrong\\u003e33\\u003c/strong\\u003e(4):570-591.\\u003c/li\\u003e\\n\\u003cli\\u003eBolger N, Davis A, Rafaeli E: \\u003cstrong\\u003eDiary methods: capturing life as it is lived\\u003c/strong\\u003e. \\u003cem\\u003eAnnual review of psychology \\u003c/em\\u003e2003, \\u003cstrong\\u003e54\\u003c/strong\\u003e:579-616.\\u003c/li\\u003e\\n\\u003cli\\u003eCreswell JW, Plano Clark VL: \\u003cstrong\\u003eDesigning and conducting mixed methods research\\u003c/strong\\u003e. Thousand Oaks, Calif.: SAGE Publ.; 2006.\\u003c/li\\u003e\\n\\u003cli\\u003eElm Ev, Altman DG, Egger M, Pocock SJ, G\\u0026oslash;tzsche PC, Vandenbroucke JP: \\u003cstrong\\u003eStrengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies\\u003c/strong\\u003e. 2007, \\u003cstrong\\u003e335\\u003c/strong\\u003e(7624):806-808.\\u003c/li\\u003e\\n\\u003cli\\u003eMonsalves MJ, Bangdiwala AS, Thabane A, Bangdiwala SI: \\u003cstrong\\u003eLEVEL (Logical Explanations \\u0026amp; Visualizations of Estimates in Linear mixed models): recommendations for reporting multilevel data and analyses\\u003c/strong\\u003e. \\u003cem\\u003eBMC Medical Research Methodology \\u003c/em\\u003e2020, \\u003cstrong\\u003e20\\u003c/strong\\u003e(1):3.\\u003c/li\\u003e\\n\\u003cli\\u003eSteelman LA, Levy PE, Snell AF: \\u003cstrong\\u003eThe Feedback Environment Scale: Construct Definition, Measurement, and Validation\\u003c/strong\\u003e. \\u003cem\\u003eEducational and Psychological Measurement \\u003c/em\\u003e2004, \\u003cstrong\\u003e64\\u003c/strong\\u003e(1):165-184.\\u003c/li\\u003e\\n\\u003cli\\u003eGiesbers APM, Schouteten RLJ, Poutsma E, van der Heijden BIJM, van Achterberg T: \\u003cstrong\\u003eTowards a better understanding of the relationship between feedback and nurses\\u0026rsquo; work engagement and burnout: A convergent mixed-methods study on nurses\\u0026rsquo; attributions about the \\u0026lsquo;why\\u0026rsquo; of feedback\\u003c/strong\\u003e. \\u003cem\\u003eInternational Journal of Nursing Studies \\u003c/em\\u003e2021, \\u003cstrong\\u003e117\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eR: A Language and Environment for Statistical Computing \\u003c/strong\\u003e[https://www.R-project.org/]\\u003c/li\\u003e\\n\\u003cli\\u003ePosit team: \\u003cstrong\\u003eRStudio: Integrated Development Environment for R.\\u003c/strong\\u003e In\\u003cem\\u003e.\\u003c/em\\u003e Boston, MA: Posit Software, PBC; 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eSommet N, Morselli D: \\u003cstrong\\u003eKeep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS\\u003c/strong\\u003e. \\u003cem\\u003eInternational Review of Social Psychology \\u003c/em\\u003e2017.\\u003c/li\\u003e\\n\\u003cli\\u003eOlvera Astivia OL, Gadermann A, Guhn M: \\u003cstrong\\u003eThe relationship between statistical power and predictor distribution in multilevel logistic regression: a simulation-based approach\\u003c/strong\\u003e. \\u003cem\\u003eBMC Medical Research Methodology \\u003c/em\\u003e2019, \\u003cstrong\\u003e19\\u003c/strong\\u003e(1):97.\\u003c/li\\u003e\\n\\u003cli\\u003eBates D, M\\u0026auml;chler M, Bolker B, Walker S: \\u003cstrong\\u003eFitting Linear Mixed-Effects Models Using lme4\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Statistical Software \\u003c/em\\u003e2015, \\u003cstrong\\u003e67\\u003c/strong\\u003e(1):1 - 48.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eCentering in Multilevel Regression \\u003c/strong\\u003e[http://web.pdx.edu/~newsomj/mlrclass/ho_centering.pdf]\\u003c/li\\u003e\\n\\u003cli\\u003eBozdogan H: \\u003cstrong\\u003eModel selection and Akaike\\u0026apos;s information criterion (AIC): The general theory and its analytical extensions\\u003c/strong\\u003e. \\u003cem\\u003ePsychometrika \\u003c/em\\u003e1987, \\u003cstrong\\u003e52\\u003c/strong\\u003e(3):345-370.\\u003c/li\\u003e\\n\\u003cli\\u003eGelman A, Hill J, Yajima M: \\u003cstrong\\u003eWhy We (Usually) Don\\u0026apos;t Have to Worry About Multiple Comparisons\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Research on Educational Effectiveness \\u003c/em\\u003e2012, \\u003cstrong\\u003e5\\u003c/strong\\u003e(2):189-211.\\u003c/li\\u003e\\n\\u003cli\\u003evan Buuren S, Groothuis-Oudshoorn K: \\u003cstrong\\u003emice: Multivariate Imputation by Chained Equations in R\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Statistical Software \\u003c/em\\u003e2011, \\u003cstrong\\u003e45\\u003c/strong\\u003e(3):1 - 67.\\u003c/li\\u003e\\n\\u003cli\\u003eChavent M, Kuentz-Simonet V, Liquet B, Saracco J: \\u003cstrong\\u003eClustOfVar: An R Package for the Clustering of Variables\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Statistical Software \\u003c/em\\u003e2012, \\u003cstrong\\u003e50\\u003c/strong\\u003e(13):1 - 16.\\u003c/li\\u003e\\n\\u003cli\\u003eTullis T, Albert B: \\u003cstrong\\u003eChapter 9 - Special Topics\\u003c/strong\\u003e. In: \\u003cem\\u003eMeasuring the User Experience (Second Edition).\\u003c/em\\u003e edn. Edited by Tullis T, Albert B. Boston: Morgan Kaufmann; 2013: 209-236.\\u003c/li\\u003e\\n\\u003cli\\u003eNHS Digital: \\u003cstrong\\u003eNHS Workforce Statistics - June 2022\\u003c/strong\\u003e. In\\u003cem\\u003e.\\u003c/em\\u003e: NHS Digital; 2022.\\u003c/li\\u003e\\n\\u003cli\\u003eFlores C, Elicker JD, Cubrich M: \\u003cstrong\\u003eThe Importance of Social Identity in Feedback Seeking: A Race Perspective\\u003c/strong\\u003e. In: \\u003cem\\u003eFeedback at Work.\\u003c/em\\u003e 1st ed. edn. Edited by Steelman LA, Williams JR. Cham, Switzerland: Springer; 2019: 141-162.\\u003c/li\\u003e\\n\\u003cli\\u003eBrehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, Ivers N, Grimshaw JM: \\u003cstrong\\u003ePractice Feedback Interventions: 15 Suggestions for Optimizing Effectiveness\\u003c/strong\\u003e. \\u003cem\\u003eAnnals of internal medicine \\u003c/em\\u003e2016, \\u003cstrong\\u003e164\\u003c/strong\\u003e(6):435-441.\\u003c/li\\u003e\\n\\u003cli\\u003eOhly S, Sonnentag S, Niessen C, Zapf D: \\u003cstrong\\u003eDiary studies in organizational research: An introduction and some practical recommendations\\u003c/strong\\u003e. \\u003cem\\u003eJournal of Personnel Psychology \\u003c/em\\u003e2010, \\u003cstrong\\u003e9\\u003c/strong\\u003e(2):79-93.\\u003c/li\\u003e\\n\\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\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-emergency-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"emmd\",\"sideBox\":\"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/emmd\",\"title\":\"BMC Emergency Medicine\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Feedback, prehospital care, emergency medical services, professional development, staff wellbeing, diary methods, multilevel modelling\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4014306/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4014306/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eProviding feedback to healthcare professionals and organisations on performance or patient outcomes may improve care quality and professional development, particularly in Emergency Medical Services (EMS) where professionals make autonomous, complex decisions and current feedback provision is limited. This study aimed to determine the content and outcomes of feedback in EMS by measuring feedback prevalence, identifying predictors of receiving feedback, categorising feedback outcomes and determining predictors of feedback efficacy.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eAn observational mixed-methods study was used. EMS professionals delivering face-to-face patient care in the United Kingdom\\u0026rsquo;s National Health Service completed a baseline survey and diary entries between March-August 2022. Diary entries were event-contingent and collected when a participant identified they had received feedback. Self-reported data were collected on feedback frequency, environment, characteristics and outcomes. Feedback environment was measured using the Feedback Environment Scale. Feedback outcomes were categorised using hierarchical cluster analysis. Multilevel logistic regression was used to assess which variables predicted feedback receipt and efficacy. Qualitative data were analysed using content analysis.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003e299 participants completed baseline surveys and 105 submitted 538 diary entries. 215 (71.9%) participants had received feedback in the last 30 days, with patient outcome feedback the most frequent (n\\u0026thinsp;=\\u0026thinsp;149, 42.8%). Feedback format was predominantly verbal (n\\u0026thinsp;=\\u0026thinsp;157, 73.0%) and informal (n\\u0026thinsp;=\\u0026thinsp;189, 80.4%). Significant predictors for receiving feedback were a paramedic role (aOR 3.04 [1.14, 8.00]), a workplace with a positive feedback-seeking culture (aOR 1.07 [1.04, 1.10]) and white ethnicity (aOR 5.68 [1.01, 29.73]). Diary entries reported feedback as very useful (median 6, IQR 5\\u0026ndash;7). Feedback outcomes included: personal wellbeing (closure, confidence and job satisfaction), professional development (clinical practice and knowledge) and service outcomes (patient care and patient safety). Feedback-seeking behaviour and higher scores on the Feedback Environment Scale were statistically significant predictors of feedback efficacy. Solicited feedback improved wellbeing (aOR 3.35 [1.68, 6.60]) and professional development (aOR 2.58 [1.10, 5.56]) more than unsolicited feedback.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eFeedback for EMS professionals was perceived to improve personal wellbeing, professional development and service outcomes. EMS workplaces need to develop a culture that encourages feedback-seeking to strengthen the impact of feedback for EMS professionals on clinical decision-making and staff wellbeing.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Prevalence, Predictors and Outcomes of Feedback for Ems Professionals: a Mixed-methods Diary Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-13 18:47:50\",\"doi\":\"10.21203/rs.3.rs-4014306/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-07-18T11:02:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-07-18T00:22:29+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"165977621996099941375855429918783596743\",\"date\":\"2024-07-05T15:18:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"154375879516715007268611911302212632921\",\"date\":\"2024-07-03T16:24:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"173902324368414855398803574646752030931\",\"date\":\"2024-07-02T15:01:58+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-07-01T20:30:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"127885559730764205365694189098427732334\",\"date\":\"2024-06-09T15:20:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"79032732700968125829741428479205108724\",\"date\":\"2024-06-03T04:25:29+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-05-20T01:38:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"102909493103921464781159320536249800528\",\"date\":\"2024-05-10T02:44:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"103188928236592144360270358302833681385\",\"date\":\"2024-05-06T20:47:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-04-24T08:59:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-03-11T09:05:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-03-11T09:03:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-03-11T09:03:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Emergency Medicine\",\"date\":\"2024-03-04T20:33:38+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-emergency-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"emmd\",\"sideBox\":\"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/emmd\",\"title\":\"BMC Emergency Medicine\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"99999753-d1e4-423f-a763-e0d1c5899d77\",\"owner\":[],\"postedDate\":\"March 13th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-09-16T16:02:13+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4014306\",\"link\":\"https://doi.org/10.1186/s12873-024-01082-y\",\"journal\":{\"identity\":\"bmc-emergency-medicine\",\"isVorOnly\":false,\"title\":\"BMC Emergency Medicine\"},\"publishedOn\":\"2024-09-13 15:57:33\",\"publishedOnDateReadable\":\"September 13th, 2024\"},\"versionCreatedAt\":\"2024-03-13 18:47:50\",\"video\":\"\",\"vorDoi\":\"10.1186/s12873-024-01082-y\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12873-024-01082-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4014306\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4014306\",\"identity\":\"rs-4014306\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}