Engagement with an LMS-based tobacco harm reduction and smoking cessation program among frontline healthcare workers in Malawi: threshold adoption dynamics and determinants

Engagement with an LMS-based tobacco harm reduction and smoking cessation program among frontline healthcare workers in Malawi: threshold adoption dynamics and determinants | 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 Engagement with an LMS-based tobacco harm reduction and smoking cessation program among frontline healthcare workers in Malawi: threshold adoption dynamics and determinants Alexander Thomas Mboma, Bright Sibale, Dziwenji Makombe, Elias Peter Mwakilama, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8988612/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Digital learning management systems (LMS) are increasingly used to strengthen frontline healthcare worker capacity in tobacco harm reduction (THR) and smoking cessation, particularly in low- and middle-income countries (LMICs). However, implementation research has focused largely on training effectiveness rather than behavioral patterns of digital platform adoption. We characterized LMS engagement and examined determinants of training uptake among frontline healthcare workers in Malawi, testing a ‘threshold adoption’ pattern, in which participants either do not engage or complete all content, with limited intermediate participation. Methods We analyzed LMS data from 267 frontline healthcare workers in Lilongwe and Mzimba North districts participated in a blended THR and smoking cessation program comprising a one-day workshop and five asynchronous LMS modules. Outcomes included full completion (all five modules), total modules completed (0-5), and total quizzes completed (0-5). Engagement distributions were described, and coherence between module and quiz completion was assessed using Pearson correlation. Predictors of full completion were estimated using multivariable logistic regression. To test threshold adoption, we applied a two-part hurdle model: logistic regression for initiation (≥1 module vs 0) and zero-truncated Poisson regression among initiators (1-5 modules), reporting adjusted incidence rate ratios (aIRRs). Follow-up telephone feedback from non-completers (n = 26) was summarized using rapid content analysis and mapped to the Consolidated Framework for Implementation Research (CFIR). Results Engagement was polarized: 36.7% completed no modules and 38.2% completed all five. Modules and quizzes completed were strongly correlated (r = 0.777, p < 0.001). Compared with participants aged <30 years, older groups had lower odds of full completion (30-39: aOR = 0.48, 95% CI 0.24-0.98; 40-49: aOR = 0.22, 95% CI 0.08-0.60; 50+: aOR = 0.06, 95% CI 0.01-0.28). No measured variables predicted initiation. Among initiators (n = 169), participants in Mzimba North completed fewer modules than those in Lilongwe (aIRR = 0.85, 95% CI: 0.75–0.97). Reported barriers included workload constraints, connectivity limitations, and login or device challenges. Conclusion LMS engagement exhibited threshold adoption dynamics, with distinct determinants for initiation and persistence. Implementation strategies should pair early activation supports with context-sensitive mechanisms to sustain completion. Tobacco harm reduction Smoking cessation Learning management system Digital training Frontline healthcare workers Implementation science Malawi 1. Introduction Tobacco use remains a leading preventable cause of morbidity and mortality globally and contributes substantially to cardiovascular disease, cancer, and chronic respiratory illness (1). Tobacco-attributable burden is increasingly concentrated in low- and middle-income countries (LMICs) (2), where cessation services are often constrained by limited public health infrastructure and health workforce capacity (3). Strengthening competencies of frontline healthcare providers in evidence-based tobacco harm reduction (THR) and smoking cessation is therefore a public health priority. Digital learning management systems (LMS) are increasingly used as scalable and cost-effective approaches to healthcare workforce development (4, 5). Blended learning models, combining in-person workshops with asynchronous online modules, can enhance flexibility and reduce disruption to routine service delivery (6). Yet the impact of digital training depends fundamentally on learner engagement. Low engagement can compromise training fidelity and attenuate downstream effects on skills, clinical practice and patient outcomes. In implementation science, digital engagement is often implicitly treated as a ‘leaky pipe,’ with gradual attrition as learners progress through successive modules (7). However, research in online education and digital interventions increasingly describes, “threshold adoption” dynamics, in which participants either fail to engage at all or persist through most or all content (8). In settings with significant infrastructural and workload constraints such as many LMIC health facilities, the decision to invest time in an online platform may represent a high-stakes, all-or-nothing choice. If initial barriers (e.g., login difficulties, poor connectivity, competing demands) are not immediately overcome, participants may never begin; conversely, those who successfully navigate early hurdles may be sufficiently motivated or supported to complete all content. This implies that the decision to initiate engagement and the decision to continue may reflect distinct determinants and barriers. Understanding these dynamics is critical for designing effective implementation strategies, yet this phenomenon remains underexamined in the context of health workforce training in LMICs. Implementation science provides a structured lens for examining such dynamics. The Consolidated Framework for Implementation Research (CFIR) (9) identifies multiple domains influencing implementation outcomes, including: intervention characteristics (e.g., complexity, usability), inner setting (e.g., workload, learning climate), outer setting (e.g., infrastructure constraints), characteristics of individuals (e.g., motivation, self-efficacy), and implementation process (e.g., onboarding and facilitation). Applying CFIR to digital training engagement can help differentiate barriers affecting early platform uptake from those influencing sustained participation. In Malawi, a blended THR and smoking cessation curriculum was implemented among frontline healthcare workers in two districts with differing service contexts. Understanding patterns of LMS engagement is essential to optimize implementation strategies and reduce preventable non-engagement. This study aimed to: characterize the distribution of LMS engagement; assess coherence between engagement behaviors; identify predictors of full completion; test a threshold adoption hypothesis using stage-specific modeling; and contextualize findings within CFIR domains. 2. Methods 2.1. Study design and setting This was a secondary analysis of implementation data from a blended THR and smoking cessation training program delivered in Lilongwe and Mzimba North districts, Malawi. These districts include urban and semi-rural service contexts. LMS interaction data were captured between March and August 2025. 2.2. Participants The analytic sample included all 267 frontline healthcare workers enrolled in the training program who consented to research use of their data. Cadres included clinicians (Clinical Officers and Medical Assistants), nurses (Registered and Nurse Midwife Technicians), and Health Surveillance Assistants (HSAs). Two medical officers participated but they were excluded from regression analyses due to very small cell size. 2.3. Training program overview The program comprised a one-day in-person workshop followed by five asynchronous online modules delivered via an LMS. The workshop introduced THR principles, smoking cessation strategies, and LMS navigation. Core content included health effects of tobacco, evidence-based cessation approaches, counselling and communication skills, and referral pathways. The workshop also introduced the 5A strategy (Ask, Advise, Assess, Assist, Arrange) to support routine cessation care. Following the workshop, participants were invited to complete five LMS modules (approximately one hour each). Modules included didactic content, quizzes with feedback, and supplemental resources. Online modules included: · Module 1: Assessing and managing tobacco dependence. · Module 2: Pharmacological support for smoking cessation. · Module 3: Community engagement and support networks. · Module 4: Tobacco control policies and legal framework. · Module 5: Cultural and socioeconomic factors in tobacco use. Successful module completion required passing post-module assessments (≥ 70%) and completing required written components where applicable. 2.4. Measures 2.4.1. Engagement outcomes Primary outcome : Full completion, defined as a binary variable indicating whether a participant completed all five LMS modules (yes/no). Secondary outcomes : · Total modules completed (0-5). · Total quizzes completed (0-5). 2.4.2. Covariates Covariates were selected based on their potential relevance to engagement: · Gender (male/female). · Professional cadre (clinician, nurse, HSA). · District (Lilongwe, Mzimba North). · Age group (<30, 30-39, 40-49, 50+ years) · Education level (MSCE, Certificate, Diploma, Degree) · Years of professional experience (20 years) 2.4.3. Follow up call feedback (explanatory implementation component) As part of routine program follow-up, the study team conducted telephone calls with all 165 participants who did not complete all five LMS modules (including 98 non-starters and 67 partial completers) to document reasons for non-completion. Calls were conducted by two trained research assistants over a two-week period, with up to three contact attempts per participant at different times of day. A semi-structured feedback template was used to document reasons for non-completion, focusing on barriers to LMS access and participation (e.g., connectivity, time, data costs, device access, login difficulties, workload, perceived relevance, and other contextual constraints). Calls were successfully completed with 26 participants (response rate: 15.8%). Call notes were recorded verbatim where possible and transcribed into a structured database. Data were analyzed using rapid content analysis: two researchers independently reviewed all responses to identify recurring barrier categories, then met to consensus on a final coding framework. Frequencies were tabulated, and anonymized illustrative excerpts were selected to support interpretation. These data were not collected as a standalone qualitative study; rather, they were used to contextualize quantitative engagement patterns. The research team acknowledges the potential for non-response bias, as participants who were unreachable may differ systematically from those who responded. 2.5. Statistical analysis Participant characteristics and engagement outcomes were summarized using frequencies, proportions, and means as appropriate. The associations between modules completed and quizzes completed were assessed using Pearson correlation. Predictors of full completion were estimated using multivariable logistic regression, reporting adjusted odds ratios (aORs) with 95% confidence intervals (CIs). To test the threshold adoption hypothesis, we fitted a two-part hurdle model appropriate for count outcomes with excess zeros: 1. Initiation model: logistic regression predicting any engagement (≥1 module) versus no engagement (0 modules). 2. Intensity model: zero-truncated Poisson regression among participants who completed at least one module (1-5 modules) reporting as adjusted incidence rate ratios (aIRRs) with 95% CIs. Robust standard errors were used to reduce sensitivity to mild model misspecification. Medical officers (n=2) were excluded from regression models due to the small cell size. All statistical analyses were conducted using Stata version 17.0 (StataCorp, College Station, TX, USA), with statistical significance set at a two-sided p < 0.05. Ethical considerations Ethical approval was obtained from the University of Malawi Research Ethics Committee (UNIMAREC), Malawi (Approval number: P.11/24/485). The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. All participants provided written informed consent for participation and for use of anonymized engagement data for research purposes. Administrative authorization was also obtained from the District Health Offices (Lilongwe and Mzimba North). 3. Results 3.1. Participant characteristics A total of 267 frontline healthcare workers were included in the descriptive analyses. Participants were predominantly female (52.1%) and were drawn from Lilongwe (55.1%) and Mzimba North (44.9%). Cadres included HSAs (36.0%), nurses (32.6%), and clinicians (30.7%); two medical officers (0.7%) were enrolled but excluded from regression analyses due to very small cell sizes. Nearly half of the sample was aged 30-39 (49.1%), and most participants held a Diploma (43.8%). Just over two-fifths had < 5 years’ work experience (41.6%), and slightly more participants were based in rural facilities (52.1%) (Table 1). Table 1. Participant characteristics (N = 267) Characteristic Category n % Gender Female 139 52.06 Male 128 47.94 Cadre Clinician 82 30.71 HSA 96 35.96 MO 2 0.75 Nurse 87 32.58 Age group <30 years 64 23.97 30-39 years 131 49.06 40-49 years 51 19.10 50+ years 21 7.87 Education level MSCE 63 23.60 Certificate 48 17.98 Diploma 117 43.82 Degree 39 14.61 Years of experience <5 years 111 41.57 5-9 years 63 23.60 10-19 years 67 25.09 20+ years 26 9.74 Area Rural 139 52.06 Urban 128 47.94 District Lilongwe 147 55.06 Mzimba North 120 44.94 Percentages are column percentages. Totals may not equal 100% due to rounding. Medical officers (n = 2) were excluded from regression analyses. Abbreviations: HSA = Health Surveillance Assistant; MO = Medical Officer; MSCE = Malawi School Certificate of Education. 3.2. Distribution of LMS engagement and behavioral coherence Engagement exhibited a strongly polarized pattern. Of the 267 participants, 98 (36.7%) completed no modules, while 102 (38.2%) completed all five modules. Relatively few participants were distributed across intermediate completion categories (1-4 modules), supporting a bimodal engagement distribution consistent with threshold adoption dynamics (Table 2). Engagement behaviors were internally coherent. Total modules completed correlated strongly with total quizzes completed (r = 0.777, p < 0.001), indicating that progression through content was accompanied by consistent assessment participation. Age was inversely correlated with both total modules completed (r = −0.239, p < 0.001) and quizzes completed (r = −0.200, p = 0.001). Years of experience showed a weaker negative association with modules completed (r = −0.137, p = 0.025). Age and experience were strongly correlated (r = 0.708, p < 0.001) (Table 2). Table 2. LMS engagement distribution and behavioral coherence (N = 267) Variable Category n % r p-value Modules completed 0 98 36.7 1 29 10.9 2 15 5.6 3 7 2.6 4 16 6.0 5 102 38.2 Pearson correlations Total modules × Total quizzes 0.777 <0.001 Age × Total modules −0.239 <0.001 Age × Total quizzes −0.200 0.001 Experience × Total modules −0.137 0.025 Age × Experience 0.708 <0.001 Percentages are based on N = 267. Full completion defined as 5/5 modules . 3.3. Bivariate associations with full completion (all five modules) In bivariate comparisons (Supplementary Tables S1 and S2), age group was significantly associated with full completion (χ²(3) = 17.28, p = 0.0006). Completion decreased monotonically with increasing age: 67.2% among participants <30 years, 55.7% among those 30-39 years, 43.1% among those 40-49 years, and 19.1% among those aged 50+ years (Table 3). In contrast, completion did not differ significantly by education level (χ²(3) = 2.64, p = 0.450) or by years of experience group (χ²(3) = 4.11, p = 0.250) (Table 3). Table 3. Modules completion by grouping variables with chi-square tests Grouping variable Category Not completed (%) Completed (%) χ² (df) p-value Age group <30 years 32.81 67.19 17.28 (3) 0.0006 30-39 years 44.27 55.73 40-49 years 56.86 43.14 50+ years 80.95 19.05 Education level MSCE 53.97 46.03 2.64 (3) 0.450 Certificate 50.00 50.00 Diploma 41.88 58.12 Degree 46.15 53.85 Experience group <5 years 45.05 54.95 4.11 (3) 0.250 5-9 years 39.68 60.32 10-19 years 50.75 49.25 20+ years 61.54 38.46 3.4. Distribution of total modules completed (0-5) by participant subgroups Patterns of overall intensity (0-5 modules) were also most clearly differentiated by age group. The distribution showed a significant between-group difference (Kruskal-Wallis H (3) = 15.41, p = 0.002). Participants <30 years had the highest intensity (mean 3.20; median 5), whereas those aged 50+ had the lowest (mean 1.00; median 0), reflecting a shift from full completion to non-engagement with increasing age (Table 4). By contrast, total modules completed did not significantly differ by education level (H (3) = 3.47, p = 0.325) or by years of experience group (H (3) = 2.50, p = 0.475), although diploma holders and those with 5-9 years of experience showed higher mean module totals than other categories (Table 4). Table 4. Distribution of total modules completed (0-5) by grouping variables with Kruskal-Wallis tests Grouping variable Category 0 1 2 3 4 5 Mean Median H (df) p-value Age group <30 years 28.12 4.69 4.69 1.56 3.12 57.81 3.20 5 15.41 (3) 0.002 30-39 years 36.64 11.45 4.58 1.53 9.16 36.64 2.45 2 40-49 years 39.22 13.73 7.84 5.88 3.92 29.41 2.10 1 50+ years 57.14 19.05 9.52 4.76 0.00 9.52 1.00 0 Education level MSCE 44.44 14.29 3.17 0.00 1.59 36.51 2.10 1 3.47 (3) 0.325 Certificate 33.33 18.75 4.17 8.33 8.33 27.08 2.21 1 Diploma 32.48 7.69 5.98 2.56 7.69 43.59 2.76 4 Degree 41.03 5.13 10.26 0.00 5.13 38.46 2.38 2 Experience group <5 years 37.84 6.31 5.41 2.70 5.41 42.34 2.59 3 2.50 (3) 0.475 5-9 years 36.51 6.35 3.17 1.59 6.35 46.03 2.73 4 10-19 years 32.84 19.40 7.46 4.48 8.96 26.87 2.18 1 20+ years 42.31 19.23 7.69 0.00 0.00 30.77 1.88 1 Cells show within-group percentages. Kruskal-Wallis tests compare total modules completed across categories. 3.5. Multivariable determinants of completion and threshold adoption (hurdle models) 3.5.1. Predictors of full completion (multivariable logistic regression) In the multivariable logistic regression model predicting full completion (all five modules), age remained the most consistent determinant. Compared with participants aged <30 years, those aged 30-39 years had lower odds of completion (aOR = 0.479, 95% CI: 0.235-0.976; p = 0.043), with further reductions among those aged 40-49 years (aOR = 0.220, 95% CI: 0.080-0.601; p = 0.003) and 50+ years (aOR = 0.063, 95% CI: 0.014-0.282; p < 0.001) (Table 5). District was not associated with completion (Mzimba North vs Lilongwe: aOR = 0.933, 95% CI: 0.550-1.581; p = 0.796). Participants with 10-19 years of experience showed suggestive, but not statistically significant higher odds of completion than those with <5 years (aOR = 1.990, 95% CI: 0.898-4.405; p = 0.090) (Table 5). 3.5.2. Formal test of threshold adoption: initiation versus intensity To test the threshold adoption hypothesis, engagement was separated into initiation and persistence components. Initiation (≥1 module vs 0) In the logistic initiation model (Table 5), none of the measured predictors; gender, cadre, or district, were significantly associated with initiating LMS engagement. This suggests that demographic and professional characteristics did not explain whether participants began the online modules. Intensity among initiators (1-5 modules; n = 169) Persistence was modeled using Poisson regression restricted to participants who completed at least one module, with robust standard errors (Table 5). District was significantly associated with engagement intensity: participants in Mzimba North completed fewer modules than those in Lilongwe (aIRR = 0.85, 95% CI: 0.75-0.97; p = 0.018). Nurses demonstrated a suggestive, but not statistically significant higher intensity than clinicians (aIRR = 1.12, p = 0.073). These findings support the conceptual distinction between determinants of starting engagement and determinants of sustaining participation. Table 5. Regression models for LMS completion and threshold adoption Model Predictor aOR / aIRR 95% CI p-value Full logistic regression (completion) Female (vs Male) 1.276 0.740-2.202 0.381 30-39 (vs <30) 0.479 0.235-0.976 0.043 40-49 (vs <30) 0.220 0.080-0.601 0.003 50+ (vs <30) 0.063 0.014-0.282 <0.001 10-19 yrs (vs <5) 1.990 0.898-4.405 0.090 Mzimba North (vs Lilongwe) 0.933 0.550-1.581 0.796 Initiation model (≥1 vs 0) Male (vs Female) 0.86 0.51-1.45 0.577 Nurse (vs Clinician) 1.28 0.67-2.45 0.455 Mzimba North (vs Lilongwe) 1.25 0.75-2.10 0.393 Intensity model (modules 1-5) Nurse (vs Clinician) 1.12 0.99-1.28 0.073 Mzimba North (vs Lilongwe) 0.85 0.75-0.97 0.018 aOR, adjusted odds ratio; aIRR, adjusted incidence rate ratio; CI, confidence interval. Reference groups were Male (gender), clinician (cadre), and Lilongwe (district). The initiation model used logistic regression predicting any engagement (≥1 module vs 0). The intensity model used zero-truncated Poisson regression among initiators (1-5 modules; n = 169), with robust standard errors, reporting aIRRs. Both models were adjusted simultaneously for gender, cadre, and district. Medical officers (n = 2) were excluded from regression analyses due to small cell size. Statistical significance was set at two-sided p < 0.05. The table displays only selected predictors; full model outputs available in supplementary Table. 3.6. Explanatory feedback from follow-up calls among non-completers (CFIR-mapped) Follow-up feedback was obtained from 26 of the 165 non-completers (15.8%). Reported barriers clustered into five categories: workload/time constraints, connectivity/data barriers, access/login barriers, device limitations, and competing priorities. These barriers mapped primarily to CFIR inner setting (workload and lack of protected time), outer setting (connectivity and data costs), intervention characteristics (log-in friction and device compatibility), and process (insufficient early facilitation and troubleshooting) (Table 6). These barriers primarily affected early engagement, consistent with the hurdle model findings that measured demographic/professional characteristics did not explain initiation. Illustrative excerpts: “ I wanted to start, but where I stay, the network is very poor, and I could not log in again.” (Nurse, Mzimba North) “ After the workshop, work became busy, so I did not have time to do the modules.” (Clinician, Lilongwe) “The problem is I lost the phone I was using at first, so for me to log in again, it was a challenge”. (HSA, Mzimba North) “I did not have data to do the online Modules, you should have given us more data, (laughs)”. (Nurse Lilongwe). “No, I did not have any problems with connectivity; I was just busy with other things” (Clinician Lilongwe) Table 6. Participant-reported barriers among non-completers (n = 26) and CFIR mapping Barrier category Example issues reported CFIR domain Illustrative quote N (%) Workload/time constraints Busy clinic schedules; no protected time Inner setting “After the workshop, work became busy…” 6 (23.1) Connectivity/data barriers Poor network; insufficient data Outer setting “The network is very poor…” 6 (23.1) Access/login barriers Re-login problems; password changes Intervention characteristics / Process “I could not log in again…” 4 (15.4) Device limitations Shared phone; phone loss/replacement Outer setting / Intervention characteristics “I lost the phone…” 4 (15.4) Competing priorities Other duties/personal commitments Inner setting / Individual characteristics “I was just busy…” 6 (23.1) Percentages represent the proportion of respondents (n = 26) who reported each barrier category. Participants were permitted to report more than one barrier; therefore, percentages do not sum to 100%. Barrier categories were derived using rapid descriptive content analysis of semi-structured follow-up call notes. CFIR domains were assigned deductively based on thematic alignment. 4. Discussion This study provides evidence on the behavioral dynamics of LMS adoption in a blended THR and smoking cessation training program in Malawi. Engagement was bimodal, with substantial clustering at zero modules and full completion, consistent with a threshold adoption pattern. The hurdle model further indicated that predictors of initiation differed from predictors of intensity among initiators, reinforcing the conceptual distinction between ‘starting’ and ‘persisting’. The polarization in module completion contrasts with the common ‘leaky pipe’ expectation of gradual attrition across sequential content (10). Instead, the major implementation challenge may be early activation; enabling participants to cross an initial engagement barrier. In this dataset, no measured demographic or professional characteristics predicted initiation, suggesting that initiation may depend on unmeasured factors such as perceived relevance, initial motivation, digital literacy, opportunity costs, or early technical barriers (e.g., device access, connectivity, log-in friction), consistent with prior literature (11-13). The qualitative findings support this interpretation; participants commonly described connectivity, login difficulties, and competing demands as reasons for not starting. Among initiators, both professional cadre and context appeared more salient. Nurses consistently showed higher engagement than clinicians, although estimates were borderline and did not consistently meet statistical significance. This pattern may reflect differences in perceived role alignment with cessation counseling, professional development norms, or workload structure, consistent with evidence on nurses’ engagement with online learning (14, 15). In CFIR terms, these patterns may reflect interactions between characteristics of individuals (e.g., motivation, role identity) and the inner setting (e.g., learning climate, compatibility). District differences were more pronounced in the intensity model: initiators in Mzimba North completed fewer modules than those in Lilongwe. This suggests that barriers to sustained engagement may be context-specific, even when barriers to initiation are more universal and operational (16, 17). Follow-up feedback supported this interpretation, with participants commonly describing competing workload demands, connectivity/data constraints, and login/access difficulties. Plausible mechanisms include differences in internet reliability, supervisory support and facility leadership engagement (18), peer learning culture (19), and competing service delivery pressures (20, 21). Together, these findings argue for adaptive implementation strategies that explicitly address context-specific constraints affecting sustained engagement. Overall, the combined LMS analytics and follow-up call feedback suggest that threshold adoption in this setting was driven less by participant demographics and more by whether early operational and technical barriers were resolved and whether the work context supported sustained participation. 4.1. Implications for practice and research 1. Design for early activation: Strengthen onboarding and early engagement supports (e.g., guided log-in sessions, clear completion timelines, automated reminders, peer champions, micro-incentives, and integrating initiation into routine supervision). 2. Differentiate strategies by engagement stage: Pair activation supports with persistence supports (e.g., supervisor check-ins, rapid troubleshooting pathways, moderated forums, progress dashboards). 3. Context-sensitive rollout: Conduct formative assessments (connectivity, workload, leadership engagement) and tailor support by district/facility context. Future research should use mixed methods to better explain non-initiation and sustained engagement, including facility-level measures (e.g. staffing ratios, internet availability, supervisory practices) and platform usability analytics (e.g., time-on-task, dropout points), alongside in-depth qualitative interviews with both completers and non-completers to understand enablers as well as barriers. 4.2. Strengths and limitations Strengths include objective LMS-derived engagement measures, a real-world LMIC implementation context, and a hurdle modeling approach aligned with the threshold adoption hypothesis. An additional strength is the use of follow-up feedback to triangulate LMS analytics and clarify early non-engagement barriers. Limitations include observational design (no causal inference) and limited covariate measurement (e.g., no measures of digital literacy, connectivity, or motivation at baseline). The follow-up call data were programmatically collected with a low response rate (15.8%), introducing potential non-response bias; participants who were unreachable may have faced different or more severe barriers than those who responded. Additionally, the study focused on barriers to non-completion and did not systematically collect data on enablers of engagement among completers, limiting a full understanding of positive deviance. Finally, education level and years of experience were excluded from main models due to collinearity, but their bivariate associations suggest they may play indirect roles that warrant further exploration. 5. Conclusion LMS engagement in a blended THR and smoking cessation training program in Malawi was polarized, consistent with threshold adoption dynamics. Initiation was not explained by measured participant characteristics, but persistence among initiators differed by district, with contextual factors particularly connectivity and infrastructure playing a key role. Implementation strategies should therefore be two-pronged: (1) early activation mechanisms to overcome initial barriers to platform uptake (e.g., guided onboarding, immediate technical support), and (2) context-sensitive persistence supports (e.g., supervisor engagement, offline options, data bundles) to sustain engagement through completion, tailored to district-level constraints. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the University of Malawi Research Ethics Committee (UNIMAREC), Malawi (Approval number: P.11/24/485). The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. All participants provided written informed consent for participation in the training program and for the use of anonymized engagement data for research purposes. Administrative authorization was also obtained from the District Health Offices (Lilongwe and Mzimba North). Consent for publication All participants provided consent for anonymized quotations to be included in publications. Availability of data and materials The datasets generated during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Global Action to End Smoking, Inc., under Grant Number UNS-001-004. The funder had no role in study design, data collection, analysis, interpretation of data, or writing of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. 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INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2024;61. Aryee GFB, Amoadu M, Obeng P, Sarkwah HN, Malcalm E, Abraham SA, et al. Effectiveness of eLearning programme for capacity building of healthcare professionals: a systematic review. Human Resources for Health. 2024;22(1). o’Doherty D, Dromey M, Lougheed J, Hannigan A, Last J, McGrath D. Barriers and solutions to online learning in medical education–an integrative review. BMC medical education. 2018;18(1):130. Karunarathne B, Nanayakkara V, Ranasinghe E, Ranasinghe M, Ahangama S, Wickramanayake S, et al., editors. Analysis of the factors affecting successful completion of asynchronous online learning programs. 22nd European Conference on e-Learning: ECEL 2023; 2023: Academic Conferences and publishing limited. Evans AM, Ellis G, Norman S, Luke K. Patient safety education—a description and evaluation of an international, interdisciplinary e-learning programme. Nurse education today. 2014;34(2):248-51. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesEngagementwithanLMSbasedtobaccoharmreductionandsmokingcessationprogramamongfrontlinehealthcareworkersinMalawi.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 06 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 27 Feb, 2026 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. 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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-8988612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615046569,"identity":"7240a8d7-92a2-47c4-9403-e314b67b5849","order_by":0,"name":"Alexander Thomas Mboma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCTBpAWNLyIEYBx4Q1gInLYzBWhJI0FKR2ABi4dOiO7v56IYfNRJyBsfPHrzxcY9E+vywww+BttjJ6TZg12J251jazZ5jEsaSPXnJljOeSeRuvJ1mANSSbGx2AIeWGzlmN3jYJBL7GXLMpHkOALXMTgBpOZC4DY+Wm3/+SdS38b8xk/5zQCLdcHb6B4JabvO2SSTwSwBtYTggkSAvnUPAFqBfbsv2SRjOnPHG2LLngIThBumcggMJBnj8crv52M0332zkDc7nGN74caBOXn52+uYPHyrs5HBpwQQGYJUGxCoHAfkGUlSPglEwCkbBSAAAuLVko1XBUqAAAAAASUVORK5CYII=","orcid":"","institution":"Center for Development Management, Consulting and Learning Facility, P.O. Box 31810, Lilongwe 3, Malawi","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"Thomas","lastName":"Mboma","suffix":""},{"id":615046570,"identity":"60c73369-de4e-48b1-b51b-700fe92df45e","order_by":1,"name":"Bright Sibale","email":"","orcid":"","institution":"Center for Development Management, Consulting and Learning Facility, P.O. Box 31810, Lilongwe 3, Malawi","correspondingAuthor":false,"prefix":"","firstName":"Bright","middleName":"","lastName":"Sibale","suffix":""},{"id":615046571,"identity":"f653586b-4102-4487-a450-818adce304b7","order_by":2,"name":"Dziwenji Makombe","email":"","orcid":"","institution":"Sub-Sahara Health and Research Institute, P.O. Box 40371, Lilongwe 4, Malawi","correspondingAuthor":false,"prefix":"","firstName":"Dziwenji","middleName":"","lastName":"Makombe","suffix":""},{"id":615046572,"identity":"f953eca9-a8ea-48b4-8e1a-c2d5f4690ca8","order_by":3,"name":"Elias Peter Mwakilama","email":"","orcid":"","institution":"University of Malawi","correspondingAuthor":false,"prefix":"","firstName":"Elias","middleName":"Peter","lastName":"Mwakilama","suffix":""},{"id":615046573,"identity":"97e7bf50-df62-4549-ba34-d9828c7acf32","order_by":4,"name":"Hlupekile Phiri","email":"","orcid":"","institution":"Center for Development Management, Consulting and Learning Facility, P.O. Box 31810, Lilongwe 3, Malawi","correspondingAuthor":false,"prefix":"","firstName":"Hlupekile","middleName":"","lastName":"Phiri","suffix":""},{"id":615046574,"identity":"8a99e85a-8927-424b-a1c3-1b322179bcce","order_by":5,"name":"Samson Banankhu Mhango","email":"","orcid":"","institution":"Center for Development Management, Consulting and Learning Facility, P.O. Box 31810, Lilongwe 3, Malawi","correspondingAuthor":false,"prefix":"","firstName":"Samson","middleName":"Banankhu","lastName":"Mhango","suffix":""}],"badges":[],"createdAt":"2026-02-27 13:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8988612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8988612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093040,"identity":"2bb49bf4-e3ef-4349-aad1-0aac20d8b7f5","added_by":"auto","created_at":"2026-04-03 11:33:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1514584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988612/v1/42b58dc1-45a2-4342-8466-3bd5b6bc15ea.pdf"},{"id":105884447,"identity":"25518f36-899f-4fae-9339-5f2ae995023d","added_by":"auto","created_at":"2026-04-01 07:16:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18489,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesEngagementwithanLMSbasedtobaccoharmreductionandsmokingcessationprogramamongfrontlinehealthcareworkersinMalawi.docx","url":"https://assets-eu.researchsquare.com/files/rs-8988612/v1/305e666e30aea87ceb529dd2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Engagement with an LMS-based tobacco harm reduction and smoking cessation program among frontline healthcare workers in Malawi: threshold adoption dynamics and determinants","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eTobacco use remains a leading preventable cause of morbidity and mortality globally and contributes substantially to cardiovascular disease, cancer, and chronic respiratory illness (1). Tobacco-attributable burden is increasingly concentrated in low- and middle-income countries (LMICs) (2), where cessation services are often constrained by limited public health infrastructure and health workforce capacity (3). Strengthening competencies of frontline healthcare providers in evidence-based tobacco harm reduction (THR) and smoking cessation is therefore a public health priority.\u003c/p\u003e\n\u003cp\u003eDigital learning management systems (LMS) are increasingly used as scalable and cost-effective approaches to healthcare workforce development (4, 5). Blended learning models, combining in-person workshops with asynchronous online modules, can enhance flexibility and reduce disruption to routine service delivery (6). Yet the impact of digital training depends fundamentally on learner engagement. Low engagement can compromise training fidelity and attenuate downstream effects on skills, clinical practice and patient outcomes.\u003c/p\u003e\n\u003cp\u003eIn implementation science, digital engagement is often implicitly treated as a \u0026lsquo;leaky pipe,\u0026rsquo; with gradual attrition as learners progress through successive modules (7). However, research in online education and digital interventions increasingly describes, \u0026ldquo;threshold adoption\u0026rdquo; dynamics, in which participants either fail to engage at all or persist through most or all content (8). In settings with significant infrastructural and workload constraints such as many LMIC health facilities, the decision to invest time in an online platform may represent a high-stakes, all-or-nothing choice. If initial barriers (e.g., login difficulties, poor connectivity, competing demands) are not immediately overcome, participants may never begin; conversely, those who successfully navigate early hurdles may be sufficiently motivated or supported to complete all content. This implies that the decision to initiate engagement and the decision to continue may reflect distinct determinants and barriers. Understanding these dynamics is critical for designing effective implementation strategies, yet this phenomenon remains underexamined in the context of health workforce training in LMICs.\u003c/p\u003e\n\u003cp\u003eImplementation science provides a structured lens for examining such dynamics. The Consolidated Framework for Implementation Research (CFIR) (9) identifies multiple domains influencing implementation outcomes, including: intervention characteristics (e.g., complexity, usability), inner setting (e.g., workload, learning climate), outer setting (e.g., infrastructure constraints), characteristics of individuals (e.g., motivation, self-efficacy), and implementation process (e.g., onboarding and facilitation). Applying CFIR to digital training engagement can help differentiate barriers affecting early platform uptake from those influencing sustained participation.\u003c/p\u003e\n\u003cp\u003eIn Malawi, a blended THR and smoking cessation curriculum was implemented among frontline healthcare workers in two districts with differing service contexts. Understanding patterns of LMS engagement is essential to optimize implementation strategies and reduce preventable non-engagement. This study aimed to:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003echaracterize the distribution of LMS engagement;\u003c/li\u003e\n \u003cli\u003eassess coherence between engagement behaviors;\u003c/li\u003e\n \u003cli\u003eidentify predictors of full completion;\u003c/li\u003e\n \u003cli\u003etest a threshold adoption hypothesis using stage-specific modeling; and\u003c/li\u003e\n \u003cli\u003econtextualize findings within CFIR domains.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2.\tMethods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; Study design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a secondary analysis of implementation data from a blended THR and smoking cessation training program delivered in Lilongwe and Mzimba North districts, Malawi. These districts include urban and semi-rural service contexts. LMS interaction data were captured between March and August 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytic sample included all 267 frontline healthcare workers enrolled in the training program who consented to research use of their data. Cadres included clinicians (Clinical Officers and Medical Assistants), nurses (Registered and Nurse Midwife Technicians), and Health Surveillance Assistants (HSAs). Two medical officers participated but they were excluded from regression analyses due to very small cell size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp; Training program overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe program comprised a one-day in-person workshop followed by five asynchronous online modules delivered via an LMS. The workshop introduced THR principles, smoking cessation strategies, and LMS navigation. Core content included health effects of tobacco, evidence-based cessation approaches, counselling and communication skills, and referral pathways. The workshop also introduced the 5A strategy (Ask, Advise, Assess, Assist, Arrange) to support routine cessation care.\u003c/p\u003e\n\u003cp\u003eFollowing the workshop, participants were invited to complete five LMS modules (approximately one hour each). Modules included didactic content, quizzes with feedback, and supplemental resources. Online modules included:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e· Module 1: Assessing and managing tobacco dependence.\u003c/p\u003e\n\u003cp\u003e· Module 2: Pharmacological support for smoking cessation.\u003c/p\u003e\n\u003cp\u003e· Module 3: Community engagement and support networks.\u003c/p\u003e\n\u003cp\u003e· Module 4: Tobacco control policies and legal framework.\u003c/p\u003e\n\u003cp\u003e· Module 5: Cultural and socioeconomic factors in tobacco use.\u003c/p\u003e\n\u003cp\u003eSuccessful module completion required passing post-module assessments (≥ 70%) and completing required written components where applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp; \u0026nbsp; \u0026nbsp; Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1.\u0026nbsp; \u0026nbsp;Engagement outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary outcome\u003c/strong\u003e: Full completion, defined as a binary variable indicating whether a participant completed all five LMS modules (yes/no).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary outcomes\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e· Total modules completed (0-5).\u003c/p\u003e\n\u003cp\u003e· Total quizzes completed (0-5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2.\u0026nbsp; \u0026nbsp;Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates were selected based on their potential relevance to engagement:\u003c/p\u003e\n\u003cp\u003e· Gender (male/female).\u003c/p\u003e\n\u003cp\u003e· Professional cadre (clinician, nurse, HSA).\u003c/p\u003e\n\u003cp\u003e· District (Lilongwe, Mzimba North).\u003c/p\u003e\n\u003cp\u003e· Age group (\u0026lt;30, 30-39, 40-49, 50+ years)\u003c/p\u003e\n\u003cp\u003e· Education level (MSCE, Certificate, Diploma, Degree)\u003c/p\u003e\n\u003cp\u003e· Years of professional experience (\u0026lt; 5 years, 5-9 years, 10-19 years \u0026gt;20 years)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFollow up call feedback (explanatory implementation component)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of routine program follow-up, the study team conducted telephone calls with all 165 participants who did not complete all five LMS modules (including 98 non-starters and 67 partial completers) to document reasons for non-completion. Calls were conducted by two trained research assistants over a two-week period, with up to three contact attempts per participant at different times of day. A semi-structured feedback template was used to document reasons for non-completion, focusing on barriers to LMS access and participation (e.g., connectivity, time, data costs, device access, login difficulties, workload, perceived relevance, and other contextual constraints).\u003c/p\u003e\n\u003cp\u003eCalls were successfully completed with 26 participants (response rate: 15.8%). Call notes were recorded verbatim where possible and transcribed into a structured database. Data were analyzed using rapid content analysis: two researchers independently reviewed all responses to identify recurring barrier categories, then met to consensus on a final coding framework. Frequencies were tabulated, and anonymized illustrative excerpts were selected to support interpretation. These data were not collected as a standalone qualitative study; rather, they were used to contextualize quantitative engagement patterns. The research team acknowledges the potential for non-response bias, as participants who were unreachable may differ systematically from those who responded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.\u0026nbsp; \u0026nbsp; \u0026nbsp; Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipant characteristics and engagement outcomes were summarized using frequencies, proportions, and means as appropriate. The associations between modules completed and quizzes completed were assessed using Pearson correlation.\u003c/p\u003e\n\u003cp\u003ePredictors of full completion were estimated using multivariable logistic regression, reporting adjusted odds ratios (aORs) with 95% confidence intervals (CIs). To test the threshold adoption hypothesis, we fitted a two-part hurdle model appropriate for count outcomes with excess zeros:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Initiation model:\u003c/strong\u003e logistic regression predicting any engagement (≥1 module) versus no engagement (0 modules).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Intensity model:\u003c/strong\u003e zero-truncated Poisson regression among participants who completed at least one module (1-5 modules) reporting as adjusted incidence rate ratios (aIRRs) with 95% CIs. Robust standard errors were used to reduce sensitivity to mild model misspecification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical officers (n=2) were excluded from regression models due to the small cell size. All statistical analyses were conducted using Stata version 17.0 (StataCorp, College Station, TX, USA), with statistical significance set at a two-sided p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the University of Malawi Research Ethics Committee (UNIMAREC), Malawi (Approval number: P.11/24/485). The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. All participants provided written informed consent for participation and for use of anonymized engagement data for research purposes. Administrative authorization was also obtained from the District Health Offices (Lilongwe and Mzimba North).\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e\u003cstrong\u003e3.1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Participant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 267 frontline healthcare workers were included in the descriptive analyses. Participants were predominantly female (52.1%) and were drawn from Lilongwe (55.1%) and Mzimba North (44.9%). Cadres included HSAs (36.0%), nurses (32.6%), and clinicians (30.7%); two medical officers (0.7%) were enrolled but excluded from regression analyses due to very small cell sizes. Nearly half of the sample was aged 30-39 (49.1%), and most participants held a Diploma (43.8%). Just over two-fifths had \u0026lt; 5 years\u0026rsquo; work experience (41.6%), and slightly more participants were based in rural facilities (52.1%) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Participant characteristics (N = 267)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCadre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40-49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCertificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLilongwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMzimba North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePercentages are column percentages. Totals may not equal 100% due to rounding. Medical officers (n = 2) were excluded from regression analyses. Abbreviations: HSA = Health Surveillance Assistant; MO = Medical Officer; MSCE = Malawi School Certificate of Education.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Distribution of LMS engagement and behavioral coherence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEngagement exhibited a strongly polarized pattern. Of the 267 participants, 98 (36.7%) completed no modules, while 102 (38.2%) completed all five modules. Relatively few participants were distributed across intermediate completion categories (1-4 modules), supporting a bimodal engagement distribution consistent with threshold adoption dynamics (Table 2).\u003c/p\u003e\n\u003cp\u003eEngagement behaviors were internally coherent. Total modules completed correlated strongly with total quizzes completed (r = 0.777, p \u0026lt; 0.001), indicating that progression through content was accompanied by consistent assessment participation. Age was inversely correlated with both total modules completed (r = \u0026minus;0.239, p \u0026lt; 0.001) and quizzes completed (r = \u0026minus;0.200, p = 0.001). Years of experience showed a weaker negative association with modules completed (r = \u0026minus;0.137, p = 0.025). Age and experience were strongly correlated (r = 0.708, p \u0026lt; 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. LMS engagement distribution and behavioral coherence (N = 267)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModules completed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson correlations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal modules \u0026times; Total quizzes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026times; Total modules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026times; Total quizzes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperience \u0026times; Total modules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026times; Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePercentages are based on N = 267. Full completion defined as 5/5 modules\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Bivariate associations with full completion (all five modules)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn bivariate comparisons (Supplementary Tables S1 and S2), age group was significantly associated with full completion (\u0026chi;\u0026sup2;(3) = 17.28, p = 0.0006). Completion decreased monotonically with increasing age: 67.2% among participants \u0026lt;30 years, 55.7% among those 30-39 years, 43.1% among those 40-49 years, and 19.1% among those aged 50+ years (Table 3). In contrast, completion did not differ significantly by education level (\u0026chi;\u0026sup2;(3) = 2.64, p = 0.450) or by years of experience group (\u0026chi;\u0026sup2;(3) = 4.11, p = 0.250) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Modules completion by grouping variables with chi-square tests\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrouping variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot completed (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2; (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.28 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40-49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.64 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCertificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperience group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.11 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Distribution of total modules completed (0-5) by participant subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatterns of overall intensity (0-5 modules) were also most clearly differentiated by age group. The distribution showed a significant between-group difference (Kruskal-Wallis H (3) = 15.41, p = 0.002). Participants \u0026lt;30 years had the highest intensity (mean 3.20; median 5), whereas those aged 50+ had the lowest (mean 1.00; median 0), reflecting a shift from full completion to non-engagement with increasing age (Table 4).\u0026nbsp;By contrast, total modules completed did not significantly differ by education level (H (3) = 3.47, p = 0.325) or by years of experience group (H (3) = 2.50, p = 0.475), although diploma holders and those with 5-9 years of experience showed higher mean module totals than other categories (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Distribution of total modules completed (0-5) by grouping variables with Kruskal-Wallis tests\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrouping variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.41 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40-49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.47 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCertificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperience group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.50 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCells show within-group percentages. Kruskal-Wallis tests compare total modules completed across categories.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Multivariable determinants of completion and threshold adoption (hurdle models)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1. \u0026nbsp; Predictors of full completion (multivariable logistic regression)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable logistic regression model predicting full completion (all five modules), age remained the most consistent determinant. Compared with participants aged \u0026lt;30 years, those aged 30-39 years had lower odds of completion (aOR = 0.479, 95% CI: 0.235-0.976; p = 0.043), with further reductions among those aged 40-49 years (aOR = 0.220, 95% CI: 0.080-0.601; p = 0.003) and 50+ years (aOR = 0.063, 95% CI: 0.014-0.282; p \u0026lt; 0.001) (Table 5). District was not associated with completion (Mzimba North vs Lilongwe: aOR = 0.933, 95% CI: 0.550-1.581; p = 0.796). Participants with 10-19 years of experience showed suggestive, but not statistically significant higher odds of completion than those with \u0026lt;5 years (aOR = 1.990, 95% CI: 0.898-4.405; p = 0.090) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFormal test of threshold adoption: initiation versus intensity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the threshold adoption hypothesis, engagement was separated into initiation and persistence components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitiation (\u0026ge;1 module vs 0)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the logistic initiation model (Table 5), none of the measured predictors; gender, cadre, or district, were significantly associated with initiating LMS engagement. This suggests that demographic and professional characteristics did not explain whether participants began the online modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntensity among initiators (1-5 modules; n = 169)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersistence was modeled using Poisson regression restricted to participants who completed at least one module, with robust standard errors (Table 5). District was significantly associated with engagement intensity: participants in Mzimba North completed fewer modules than those in Lilongwe (aIRR = 0.85, 95% CI: 0.75-0.97; p = 0.018). Nurses demonstrated a suggestive, but not statistically significant higher intensity than clinicians (aIRR = 1.12, p = 0.073).\u003c/p\u003e\n\u003cp\u003eThese findings support the conceptual distinction between determinants of starting engagement and determinants of sustaining participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Regression models for LMS completion and threshold adoption\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eaOR / aIRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull logistic regression (completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale (vs Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.740-2.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-39 (vs \u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.235-0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40-49 (vs \u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.080-0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50+ (vs \u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014-0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19 yrs (vs \u0026lt;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.898-4.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMzimba North (vs Lilongwe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.550-1.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitiation model (\u0026ge;1 vs 0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale (vs Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNurse (vs Clinician)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.67-2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMzimba North (vs Lilongwe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75-2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntensity model (modules 1-5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNurse (vs Clinician)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMzimba North (vs Lilongwe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eaOR, adjusted odds ratio; aIRR, adjusted incidence rate ratio; CI, confidence interval. Reference groups were Male (gender), clinician (cadre), and Lilongwe (district). The initiation model used logistic regression predicting any engagement (\u0026ge;1 module vs 0). The intensity model used zero-truncated Poisson regression among initiators (1-5 modules; n = 169), with robust standard errors, reporting aIRRs. Both models were adjusted simultaneously for gender, cadre, and district. Medical officers (n = 2) were excluded from regression analyses due to small cell size. Statistical significance was set at two-sided p \u0026lt; 0.05. The table displays only selected predictors; full model outputs available in supplementary Table.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Explanatory feedback from follow-up calls among non-completers (CFIR-mapped)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollow-up feedback was obtained from 26 of the 165 non-completers (15.8%). Reported barriers clustered into five categories: workload/time constraints, connectivity/data barriers, access/login barriers, device limitations, and competing priorities. These barriers mapped primarily to CFIR inner setting (workload and lack of protected time), outer setting (connectivity and data costs), intervention characteristics (log-in friction and device compatibility), and process (insufficient early facilitation and troubleshooting) (Table 6). These barriers primarily affected early engagement, consistent with the hurdle model findings that measured demographic/professional characteristics did not explain initiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIllustrative excerpts:\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;\u003cem\u003eI wanted to start, but where I stay, the network is very poor, and I could not log in again.\u0026rdquo; (Nurse, Mzimba North)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;\u003cem\u003eAfter the workshop, work became busy, so I did not have time to do the modules.\u0026rdquo; (Clinician, Lilongwe)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The problem is I lost the phone I was using at first, so for me to log in again, it was a challenge\u0026rdquo;. (HSA, Mzimba North)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;I did not have data to do the online Modules, you should have given us more data, (laughs)\u0026rdquo;. (Nurse Lilongwe).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;No, I did not have any problems with connectivity; I was just busy with other things\u0026rdquo; (Clinician Lilongwe)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Participant-reported barriers among non-completers (n = 26) and CFIR mapping\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarrier category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExample issues reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFIR domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllustrative quote\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorkload/time constraints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBusy clinic schedules; no protected time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInner setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;After the workshop, work became busy\u0026hellip;\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConnectivity/data barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor network; insufficient data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOuter setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;The network is very poor\u0026hellip;\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAccess/login barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRe-login problems; password changes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntervention characteristics / Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;I could not log in again\u0026hellip;\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDevice limitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShared phone; phone loss/replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOuter setting / Intervention characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;I lost the phone\u0026hellip;\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompeting priorities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther duties/personal commitments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInner setting / Individual characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;I was just busy\u0026hellip;\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePercentages represent the proportion of respondents (n = 26) who reported each barrier category. Participants were permitted to report more than one barrier; therefore, percentages do not sum to 100%. Barrier categories were derived using rapid descriptive content analysis of semi-structured follow-up call notes. CFIR domains were assigned deductively based on thematic alignment.\u003c/em\u003e\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eThis study provides evidence on the behavioral dynamics of LMS adoption in a blended THR and smoking cessation training program in Malawi. Engagement was bimodal, with substantial clustering at zero modules and full completion, consistent with a threshold adoption pattern. The hurdle model further indicated that predictors of initiation differed from predictors of intensity among initiators, reinforcing the conceptual distinction between ‘starting’ and ‘persisting’.\u003c/p\u003e\n\u003cp\u003eThe polarization in module completion contrasts with the common ‘leaky pipe’ expectation of gradual attrition across sequential content (10). Instead, the major implementation challenge may be early activation; enabling participants to cross an initial engagement barrier. In this dataset, no measured demographic or professional characteristics predicted initiation, suggesting that initiation may depend on unmeasured factors such as perceived relevance, initial motivation, digital literacy, opportunity costs, or early technical barriers (e.g., device access, connectivity, log-in friction), consistent with prior literature (11-13). The qualitative findings support this interpretation; participants commonly described connectivity, login difficulties, and competing demands as reasons for not starting.\u003c/p\u003e\n\u003cp\u003eAmong initiators, both professional cadre and context appeared more salient. Nurses consistently showed higher engagement than clinicians, although estimates were borderline and did not consistently meet statistical significance. This pattern may reflect differences in perceived role alignment with cessation counseling, professional development norms, or workload structure, consistent with evidence on nurses’ engagement with online learning (14, 15). In CFIR terms, these patterns may reflect interactions between characteristics of individuals (e.g., motivation, role identity) and the inner setting (e.g., learning climate, compatibility).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDistrict differences were more pronounced in the intensity model: initiators in Mzimba North completed fewer modules than those in Lilongwe. This suggests that barriers to sustained engagement may be context-specific, even when barriers to initiation are more universal and operational (16, 17). Follow-up feedback supported this interpretation, with participants commonly describing competing workload demands, connectivity/data constraints, and login/access difficulties. Plausible mechanisms include differences in internet reliability, supervisory support and facility leadership engagement (18), peer learning culture (19), and competing service delivery pressures (20, 21).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTogether, these findings argue for adaptive implementation strategies that explicitly address context-specific constraints affecting sustained engagement. Overall, the combined LMS analytics and follow-up call feedback suggest that threshold adoption in this setting was driven less by participant demographics and more by whether early operational and technical barriers were resolved and whether the work context supported sustained participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Implications for practice and research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Design for early activation:\u003c/strong\u003e Strengthen onboarding and early engagement supports (e.g., guided log-in sessions, clear completion timelines, automated reminders, peer champions, micro-incentives, and integrating initiation into routine supervision).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Differentiate strategies by engagement stage:\u003c/strong\u003e Pair activation supports with persistence supports (e.g., supervisor check-ins, rapid troubleshooting pathways, moderated forums, progress dashboards).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;Context-sensitive rollout:\u003c/strong\u003e Conduct formative assessments (connectivity, workload, leadership engagement) and tailor support by district/facility context.\u003c/p\u003e\n\u003cp\u003eFuture research should use mixed methods to better explain non-initiation and sustained engagement, including facility-level measures (e.g. staffing ratios, internet availability, supervisory practices) and platform usability analytics (e.g., time-on-task, dropout points), alongside in-depth qualitative interviews with both completers and non-completers to understand enablers as well as barriers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Strengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrengths include objective LMS-derived engagement measures, a real-world LMIC implementation context, and a hurdle modeling approach aligned with the threshold adoption hypothesis. An additional strength is the use of follow-up feedback to triangulate LMS analytics and clarify early non-engagement barriers. Limitations include observational design (no causal inference) and limited covariate measurement (e.g., no measures of digital literacy, connectivity, or motivation at baseline). The follow-up call data were programmatically collected with a low response rate (15.8%), introducing potential non-response bias; participants who were unreachable may have faced different or more severe barriers than those who responded. Additionally, the study focused on barriers to non-completion and did not systematically collect data on enablers of engagement among completers, limiting a full understanding of positive deviance. Finally, education level and years of experience were excluded from main models due to collinearity, but their bivariate associations suggest they may play indirect roles that warrant further exploration.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eLMS engagement in a blended THR and smoking cessation training program in Malawi was polarized, consistent with threshold adoption dynamics. Initiation was not explained by measured participant characteristics, but persistence among initiators differed by district, with contextual factors particularly connectivity and infrastructure playing a key role. Implementation strategies should therefore be two-pronged: (1) early activation mechanisms to overcome initial barriers to platform uptake (e.g., guided onboarding, immediate technical support), and (2) context-sensitive persistence supports (e.g., supervisor engagement, offline options, data bundles) to sustain engagement through completion, tailored to district-level constraints.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the University of Malawi Research Ethics Committee (UNIMAREC), Malawi (Approval number: P.11/24/485). The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. All participants provided written informed consent for participation in the training program and for the use of anonymized engagement data for research purposes. Administrative authorization was also obtained from the District Health Offices (Lilongwe and Mzimba North).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided consent for anonymized quotations to be included in publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Global Action to End Smoking, Inc., under Grant Number UNS-001-004. The funder had no role in study design, data collection, analysis, interpretation of data, or writing of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eATM conceived the study. EPM led data analysis, SM contributed to interpretation of findings. ATM drafted the manuscript. All authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the frontline healthcare workers in Lilongwe and Mzimba North districts who participated in this training program. We also thank Lilongwe and Mzimba North District Health Offices, Project Officers and LMS technical team for their support during program delivery and data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoy A, Rawal I, Jabbour S, Prabhakaran D. Tobacco and Cardiovascular Disease: A Summary of Evidence. The World Bank; 2017. p. 57-77.\u003c/li\u003e\n\u003cli\u003eTheilmann M, Lemp JM, Winkler V, Manne-Goehler J, Marcus ME, Probst C, et al. Patterns of tobacco use in low and middle income countries by tobacco product and sociodemographic characteristics: nationally representative survey data from 82 countries. BMJ. 2022;378:e067582.\u003c/li\u003e\n\u003cli\u003eAl-Worafi YM. Smoking Cessation in Developing Countries: Challenges and Recommendations. Springer International Publishing; 2024. p. 1-20.\u003c/li\u003e\n\u003cli\u003eAhmad Fuad AS, Ooi ECW, Ahmad A, Mohd Marzuki N. Digital transformation for sustainable healthcare education: Evaluating the impact of Moodle learning management system on ICD-11 training. Informatics and Health. 2026;3(1):10-8.\u003c/li\u003e\n\u003cli\u003eKamath SG, Nayak KR, Nayak V, Verma S. Leveraging learning management systems in medical education: a scoping review of use, outcomes, and improvement pathways. Medical Education Online. 2025;30(1).\u003c/li\u003e\n\u003cli\u003eHege I, Tolks D, Adler M, H\u0026auml;rtl A. Blended learning: ten tips on how to implement it into a curriculum in healthcare education. GMS J Med Educ. 2020;37(5):Doc45.\u003c/li\u003e\n\u003cli\u003eHekler EB, Michie S, Pavel M, Rivera DE, Collins LM, Jimison HB, et al. Advancing Models and Theories for Digital Behavior Change Interventions. American Journal of Preventive Medicine. 2016;51(5):825-32.\u003c/li\u003e\n\u003cli\u003eDritsas E, Trigka M. Methodological and Technological Advancements in E-Learning. Information. 2025;16(1):56.\u003c/li\u003e\n\u003cli\u003eDamschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science. 2009;4(1):50.\u003c/li\u003e\n\u003cli\u003eSerth S, Staubitz T, van Elten M, Meinel C. Measuring the effects of course modularizations in online courses for life-long learners. Frontiers in Education. 2022;Volume 7 - 2022.\u003c/li\u003e\n\u003cli\u003eLan M, Tao S, Pan Q, Liang Q, Tan CY, Law NW. Three-year development of digital literacy among primary and secondary school students: the role of digital activity participation and demographic characteristics. Interactive Learning Environments. 2026:1-20.\u003c/li\u003e\n\u003cli\u003eRuiu ML, Ruiu G, Ragnedda M. Beyond Access: Motivation and Digital Literacy in Sustainable Technology Use. International Journal of Communication. 2025;19:25-.\u003c/li\u003e\n\u003cli\u003eStarke S, Ludviga I. Unlocking Digital Potential\u0026mdash;The Impact of Innovation and Self-Determined Learning. Systems. 2025;13(5):396.\u003c/li\u003e\n\u003cli\u003eRiley K, Schmidt D. Does online learning click with rural nurses? A qualitative study. Australian Journal of Rural Health. 2016;24(4):265-70.\u003c/li\u003e\n\u003cli\u003eAlleaume D, Benrabah YC, Allagb\u0026eacute; I, Masure M, Depoux A, Mal\u0026eacute;cot M, et al. Empowering healthcare professionals to help smokers quit: Relevance of a smoking cessation online training program. Public Health in Practice. 2026;11:100699.\u003c/li\u003e\n\u003cli\u003eBorges Do Nascimento IJ, Abdulazeem H, Vasanthan LT, Martinez EZ, Zucoloto ML, \u0026Oslash;stengaard L, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digital Medicine. 2023;6(1).\u003c/li\u003e\n\u003cli\u003ePenkunas MJ, Ross B, Scott CP, Thorson A, Baron LF, Rebai WK, et al. Barriers to Applying Knowledge Gained Through an Implementation Research Massive Open Online Course: An Explanatory Qualitative Study. INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2024;61.\u003c/li\u003e\n\u003cli\u003eAryee GFB, Amoadu M, Obeng P, Sarkwah HN, Malcalm E, Abraham SA, et al. Effectiveness of eLearning programme for capacity building of healthcare professionals: a systematic review. Human Resources for Health. 2024;22(1).\u003c/li\u003e\n\u003cli\u003eo\u0026rsquo;Doherty D, Dromey M, Lougheed J, Hannigan A, Last J, McGrath D. Barriers and solutions to online learning in medical education\u0026ndash;an integrative review. BMC medical education. 2018;18(1):130.\u003c/li\u003e\n\u003cli\u003eKarunarathne B, Nanayakkara V, Ranasinghe E, Ranasinghe M, Ahangama S, Wickramanayake S, et al., editors. Analysis of the factors affecting successful completion of asynchronous online learning programs. 22nd European Conference on e-Learning: ECEL 2023; 2023: Academic Conferences and publishing limited.\u003c/li\u003e\n\u003cli\u003eEvans AM, Ellis G, Norman S, Luke K. Patient safety education\u0026mdash;a description and evaluation of an international, interdisciplinary e-learning programme. Nurse education today. 2014;34(2):248-51.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tobacco harm reduction, Smoking cessation, Learning management system, Digital training, Frontline healthcare workers, Implementation science, Malawi","lastPublishedDoi":"10.21203/rs.3.rs-8988612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8988612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital learning management systems (LMS) are increasingly used to strengthen frontline healthcare worker capacity in tobacco harm reduction (THR) and smoking cessation, particularly in low- and middle-income countries (LMICs). However, implementation research has focused largely on training effectiveness rather than behavioral patterns of digital platform adoption. We characterized LMS engagement and examined determinants of training uptake among frontline healthcare workers in Malawi, testing a ‘threshold adoption’ pattern, in which participants either do not engage or complete all content, with limited intermediate participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed LMS data from 267 frontline healthcare workers in Lilongwe and Mzimba North districts participated in a blended THR and smoking cessation program comprising a one-day workshop and five asynchronous LMS modules. Outcomes included full completion (all five modules), total modules completed (0-5), and total quizzes completed (0-5). Engagement distributions were described, and coherence between module and quiz completion was assessed using Pearson correlation. Predictors of full completion were estimated using multivariable logistic regression. To test threshold adoption, we applied a two-part hurdle model: logistic regression for initiation (≥1 module vs 0) and zero-truncated Poisson regression among initiators (1-5 modules), reporting adjusted incidence rate ratios (aIRRs). Follow-up telephone feedback from non-completers (n = 26) was summarized using rapid content analysis and mapped to the Consolidated Framework for Implementation Research (CFIR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEngagement was polarized: 36.7% completed no modules and 38.2% completed all five. Modules and quizzes completed were strongly correlated (r = 0.777, p \u0026lt; 0.001). Compared with participants aged \u0026lt;30 years, older groups had lower odds of full completion (30-39: aOR = 0.48, 95% CI 0.24-0.98; 40-49: aOR = 0.22, 95% CI 0.08-0.60; 50+: aOR = 0.06, 95% CI 0.01-0.28). No measured variables predicted initiation. Among initiators (n = 169), participants in Mzimba North completed fewer modules than those in Lilongwe (aIRR = 0.85, 95% CI: 0.75–0.97). Reported barriers included workload constraints, connectivity limitations, and login or device challenges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMS engagement exhibited threshold adoption dynamics, with distinct determinants for initiation and persistence. Implementation strategies should pair early activation supports with context-sensitive mechanisms to sustain completion.\u003c/p\u003e","manuscriptTitle":"Engagement with an LMS-based tobacco harm reduction and smoking cessation program among frontline healthcare workers in Malawi: threshold adoption dynamics and determinants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 07:16:29","doi":"10.21203/rs.3.rs-8988612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-02T10:17:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T12:09:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T10:35:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T10:31:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-02-27T13:27:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0eb9875e-0e3d-43cb-b917-bb2dea16af6e","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T10:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 07:16:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8988612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8988612","identity":"rs-8988612","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}