Learning in patient-based education sessions: A prospective evaluation*
Bibliographic record
Abstract
OBJECTIVE: The educational impact of on-call scheduling for physicians in training is not well understood. The benefit of patient-based educational sessions during postcall periods may be enhanced by the greater patient familiarity associated with long on-call shifts, or it may be attenuated by fatigue. The objective was to evaluate the impact of in-house call on cognitive attention, learning, and recall of critical care medicine trainees, before and after a reduction in call period. DESIGN: A prospective before and after survey during 8 wks in 2004. SETTING: Two critical care units at the Hospital for Sick Children, Toronto. PARTICIPANTS: Trainees in a university-affiliated critical care medicine program at the Hospital for Sick Children. INTERVENTIONS: Duty hour reduction from 26.5-hr to 18-hr on-call shifts. MEASUREMENTS AND MAIN RESULTS: Likert scales were completed after morning educational seminars self-reporting alertness, concentration, how well discussions were followed, and the acquisition of new or practice changing knowledge. Respondents were classified according to how recently they had been on call. Eleven trainees completed 231 questionnaires (80% response rate). Fellows with more recent on-call periods had reduced concentration (p = .002), alertness (p < .0001), and recall of the previous session (p = .009) and followed discussions less well (p = .019). Eighteen-hour shifts were associated with increased postcall alertness (p = .002), concentration (p = .03), and assimilation of discussions (p = .045). However, neither the duration of call nor the length of time since being on call was associated with differences in the acquisition of new theoretical or practice-changing knowledge. CONCLUSIONS: Reduced mental attention after being on call is more pronounced after longer shifts. Learning was not affected by shift duration or by how recently trainees were on call. Increased patient familiarity does not augment learning in patient-based medical education.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".