Perceived barriers to completing an e-learning program onevidence-based medicine
Bibliographic record
Abstract
PURPOSE: The Continuing Professional Development Center of the Faculty of Medicine at Laval University offers an internet-based program on evidence-based medicine (EBM). After one year, only three physicians out of the 40 who willingly paid to register had completed the entire program. This descriptive study aimed to identify physicians' beliefs regarding their completion of this online program. METHODS: Using theoretical concepts from the Theory of Planned Behaviour, a semi-structured telephone interview guide was developed to assess respondents' attitudes, perceived subjective norms, perceived obstacles and facilitating conditions with respect to completing this internet-based program. Three independent reviewers performed content analysis of the interview transcripts to obtain an appropriate level of reliability. Findings were shared and organised according to theoretical categories of beliefs. RESULTS: A total of 35 physicians (88% response rate) were interviewed. Despite perceived advantages to completing the internet-based program, barriers remained, especially those related to physicians' perceptions of time constraints. Lack of personal discipline and unfamiliarity with computers were also perceived as important barriers. CONCLUSIONS: This study offers a theoretical basis to understand physicians' beliefs towards completing an internet-based continuing medical education (CME) program on EBM. Based upon respondents' insights, several modifications were carried out to enhance the uptake of the program by physicians and, therefore, its implementation.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".