Learning-by-Concordance for Family Physicians: Revealing its Value for Continuing Professional Development in Dermatology
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Introduction Continuous Professional Development (CPD) is an important part of a physician's professional life. Yet, providing effective in-service training solutions is a persistent challenge for CPD planners. Methods Primary care physicians are frequently confronted with skin lesionsthey feel ill-prepared to manage. A dermatology Learning-by-concordance (LbC) online activity was developed and offered to family physicians for CPD credit. We were interested in finding out whether this online tool was suitable for CPD. Following a pilot phase, the on-line activity was launched and 45 geographically dispersed primary care physicians completed it. They participated in a telephone conference a week later with an expert to discuss outstanding questions. Evaluation was carried out by a survey that was available immediately after the last case. Results Participants found the on-line training tool user friendly and should be implemented on a larger scale. Participants found the dermatology concepts discussed allowed them to increase their knowledge and apply it to their practice. Discussion Among the strengths of LbC is that the learning task resemble those of a primary physician's daily practice. Finally, our study reveals that LbC is easily integrated in busy work schedules and thus is an effective learning solution for CPD.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".