Towards the identification of family physician learning needs through a reflective process
Bibliographic record
Abstract
Background Continuing professional development stakeholders are continually searching for better ways of collecting and using data to determine the educational needs of physicians. Research questions1) What, if any, family physician learning needs are revealed through the reflective process prompted by the Information Assessment Method (IAM)?2) What is the meaning of the Highlight ratings for the identification and prioritization of Canadian family physician learning needs? Methods A mixed methods sequential explanatory design was employed. Quantitative IAM data was collected from a family medicine web based e-Therapeutics+ 'Highlights' continuing medical education program over a 22-week period. Six senior Canadian continuing professional development key informants were interviewed about the meaning and potential uses of this IAM data in the context of current needs assessment practices. Results 3690 family physicians rated at least one highlight (31.4% participation rate). A mean of 675.2 (range 414-1176) ratings per highlight was recorded. On average, 54.5 % of participants learned something new, 45.7 % were motivated to learn more and 59.3% found topics to be relevant to at least one patient in practice. Key informants found that ratings 'motivation to learn more' may suggest participants' learning needs when combined with data from other sources, and that 'learning' and 'relevance' ratings can reveal information about participants' knowledge base. Conclusions With data from other sources, IAM data may suggest learning needs, and reveal topics where physician knowledge was confirmed.
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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 | 0.126 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".