Assessing the Relationship of Learning Approaches to Workplace Climate in Clerkship and Residency
Bibliographic record
Abstract
PURPOSE: To determine what approaches to learning are adopted by clinical clerks and residents and whether these approaches are associated with demographic factors, specialty, level of training, and perceptions of the workplace climate. METHOD: In 2001-02, medical clerks (n = 532) and residents (n = 2,939) at five medical schools in Ontario, Canada, were mailed the Workplace Learning Questionnaire. The correlation between the approaches to learning at work and perceived workplace climate and the influence of gender, age, location, residency program and level of training on outcomes were measured. RESULTS: A total of 1,642 clerks and residents responded (47%). The factor structure and reliability of the Workplace Learning Questionnaire were confirmed for these respondents. A surface-disorganized approach to learning was correlated with perception of heavy workload (r = .401, p < .001). The deep approach to learning was correlated with perception of choice-independence in the workplace and a supportive-receptive workplace (r = .32, p < .001; r = .23, p < .001). The climate factors, perception of choice-independence and supportive-receptive workplace, were correlated (r = .60, p < .001). There were significant differences among the mean scores for scales based on residency, year of training, and location of training. CONCLUSIONS: Perception of the workplace climate was associated with the approach to learning in the workplace of clerks and residents. Perception of heavy workload was associated with less effective approaches to learning. These associations varied with the residency program and the level of training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".