Learning portfolio models in health regulatory colleges of Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Health regulatory colleges promote continued competence by requiring members to submit yearly portfolios that document learning. Previous studies conclude that portfolios can be valuable tools to promote continuous learning in health college members, but portfolios are time-consuming to complete and difficult to evaluate. This exploratory study compares the features of portfolio models in regulatory colleges, as a basis for future studies. METHODS: Data were collected through a document review of the portfolio models described on the Web sites of 14 Canadian health regulatory colleges. RESULTS: All models contain 3 common components of self-directed learning: (1) self-diagnosis, (2) learning plan and activities, and (3) self-evaluation. Several include member profiles and peer feedback. A broad range of formal, nonformal, and informal activities are accepted as evidence of learning; a few colleges restrict learners' freedom in selecting these activities. DISCUSSION: There is a dual philosophy of learning in portfolio models that includes both humanist and technical paradigms. Low numbers of members are selected for audit of completed portfolios. The possibility of last-minute preparation and the lack of support to members who struggle with self-directed learning methods are issues to be resolved. Although portfolios are designed to enhance learning and reflection, quality cannot be ensured unless compliance is enforced, and learning outcomes are measured. Professionals should be guided regarding how to complete portfolios. More health regulatory colleges should announce the number of portfolios they audit. In general, the number of portfolios audited by each profession may need to be increased.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".