A study of a multi‐source feedback system for international medical graduates holding defined licences
Bibliographic record
Abstract
OBJECTIVE: To develop and assess the feasibility and psychometric properties of multi-source feedback questionnaires to monitor international medical graduates practising in Canada under 'defined' licences. METHOD: Four questionnaires (patient, co-worker, colleague and self) were developed and administered in 2 phases through paper-based and telephone or Internet formats. Reliability was assessed with Cronbach's alpha and generalisability coefficient analyses. Validity was established through mean ratings, 'unable to respond' rates and factor analyses. RESULTS: A total of 37 doctors participated in the 2 phases. Overall response rates were 70% for patients, 86% for co-workers, 72% for medical colleagues and 92% for self, with response rates higher for the paper-based format than the Internet and phone formats. The instruments had high internal consistency reliability, with Cronbach's alphas of 0.83 for self-assessment and > 0.90 for the other instruments. The generalisability coefficients were Ep(2) = 0.71 for 25 patients on a 13-item survey, Ep(2) = 0.59 for 8 co-workers on a 13-item survey, and Ep(2) = 0.67 for 8 colleagues on a 21-item questionnaire. The range of mean scores was narrow (between 4 and 5) for all items and all surveys. The factor analyses identified that 2 factors accounted for 70% or more of the variance for the patient and colleague surveys and 60% of the variance for the co-worker survey. CONCLUSION: These data suggest that the instruments have reasonable psychometric properties. Traditional survey methods (i.e. paper-based) yielded better results than Internet or phone methods for this group of doctors.
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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 | 0.016 | 0.085 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".