Reading of the Week: a continuing professional development program for psychiatrists and residents that Osler would have liked
Bibliographic record
Abstract
Introduction: William Osler started the first journal club more than a century ago. As in Osler's time, continuing professional development (CPD) is challenging to deliver in our day. This paper discusses the CPD outcomes of Reading of the Week (ROTW), an innovative online education resource aimed at Canadian psychiatrists and psychiatry residents. Methods: ROTW consists of a weekly email sent to these physicians through formal partnerships, including 13 residency training programs, and summarizes the latest literature in psychiatric care. An online survey using Moore's continued medical education evaluation framework was conducted to determine the outcomes of ROTW and how to improve it. Results: = 332) responded to the survey. Respondents reported a very high rate of satisfaction (97%). The most significant findings: ROTW improved participants' understanding of psychiatry (93%) and informed their practice (83%). Conclusions: ROTW is a program that addresses challenges related to remaining "up-to-date" amidst the vast amount of resources available. Survey data suggests that ROTW has a high satisfaction rate and achieves practice change, perhaps because it provides a boundless learning option for trainees and providers. Further research is needed better to understand the reasons for the success of this program.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".