Innovative Mobile App (CPD By the Minute) for Continuing Professional Development in Medicine: Multimethods Study
Bibliographic record
Abstract
BACKGROUND: Many national medical governing bodies encourage physicians to engage in continuing professional development (CPD) activities to cultivate their knowledge and skills to ensure their clinical practice reflects the current standards and evidence base. However, physicians often encounter various barriers that hinder their participation in CPD programs, such as time constraints, a lack of centralized coordination, and limited opportunities for self-assessment. The literature has highlighted the strength of using question-based learning interventions to augment physician learning and further enable change in practice. CPD By the Minute (CPD-Min) is a smartphone-enabled web-based app that was developed to address self-assessment gaps and barriers to engagement in CPD activities. OBJECTIVE: This study aimed to assess the app using four objectives: (1) engagement and use of the app throughout the study, (2) effectiveness of this tool as a CPD activity, (3) relevance of the disseminated information to physicians' practice, and (4) acceptability to physicians of this novel tool as an educational initiative. METHODS: The CPD-Min app disseminated 2 multiple-choice questions (1-min each) each week with feedback and references. Participants included licensed staff physicians, fellows, and residents across Canada. A concurrent multimethods study was conducted, consisting of preintervention and postintervention surveys, semistructured interviews, and app analytics. Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, the qualitative data were analyzed deductively and inductively. RESULTS: Of the 105 Canadian anesthesiologists participating in the study, 89 (84.8%) were staff physicians, 12 (11.4%) were fellows, and 4 (3.8%) were residents. Participants completed 110 questions each over the course of 52 weeks, with an average completion rate of 75% (SD 33%). In total, 40.9% (43/105) of participants answered >90% of the questions, including 15.2% (16/105) who completed all questions. Moreover, 69% (52/75) of participants reported the app to be an effective and valuable resource for their practice and to enhance continuous learning. Most participants (63/75, 84%) who completed the postsurveys reported that they would likely continue using the app as a CPD tool. These findings were further supported by the interview data. Three key themes were identified: the practical design of the novel educational app facilitates its adoption by clinicians, the app was perceived as a useful knowledge tool for continuous learning, and the app's low-stakes testing environment cultivated independent learning attitudes. CONCLUSIONS: The findings suggest the potential of the app to improve longitudinal assessments that promote lifelong learning among clinicians. The positive feedback and increased acceptance of the app supports it as an innovative tool for knowledge retention and CPD. Future research efforts should prioritize evaluating the app's long-term sustainability and its impact on physicians' practice, as well as exploring alternative approaches (such as artificial intelligence-based tools) for generating questions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".