Family residents’ interventions during in-hospital call
Bibliographic record
Abstract
BACKGROUND: Family medicine residents in Canada and other countries have traditionally performed in-hospital calls. With the advent of competency-based objectives, it was necessary to document what interventions were performed. This study documented the interventions that were performed by family medicine residents at the Université de Montréal during their time on call in the hospital, the time needed for those interventions, and the extent to which they were supervised. METHODS: During or at the end of the time on call the residents completed a form detailing their interventions. These forms provided data for 116 periods of time on call. RESULTS: The most frequent activity was the evaluation of stable patients. Resuscitation and other techniques were rarely performed. Residents spent an average of 31 per cent of their time on call working; 48 per cent of technical procedures were supervised, as were 25 per cent of the other interventions. DISCUSSION: Only 35 per cent of residents agreed to participate in the study, but the similarity of the results in each of the six hospitals suggests that they are reliable. Although supervision was always available, 72 per cent of the interventions were performed autonomously, possibly because the residents appreciate and learn from that experience. Time on call is not the best time for learning procedures, as there were too few opportunities to perform them. These results suggest that the pedagogical objectives of the call system should be re-evaluated with special attention to autonomy and self-confidence. Procedures should be learned in other settings.
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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 | 0.002 | 0.017 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".