Putting performance in context: the perceived influence of environmental factors on work-based performance
Bibliographic record
Abstract
INTRODUCTION: Context shapes behaviours yet is seldom considered when assessing competence. Our objective was to explore attending physicians' and trainees' perceptions of the Internal Medicine Clinical Teaching Unit (CTU) environment and how they thought contextual factors affected their performance. METHOD: 29 individuals recently completing CTU rotations participated in nine level-specific focus groups (2 with attending physicians, 3 with senior and 2 with junior residents, and 2 with students). Participants were asked to identify environmental factors on the CTU and to describe how these factors influenced their own performance across CanMEDS roles. Discussions were analyzed using constructivist grounded theory. RESULTS: Five major contextual factors were identified: Busyness, Multiple Hats, Other People, Educational Structures, and Hospital Resources and Policies. Busyness emerged as the most important, but all factors had a substantial perceived impact on performance. Participants felt their performance on the Manager and Scholar roles was most affected by environmental factors (mostly negatively, due to decreased efficiency and impact on learning). CONCLUSIONS: In complex workplace environments, numerous factors shape performance. These contextual factors and their impact need to be considered in observations and judgements made about performance in the workplace, as without this understanding conclusions about competency may be flawed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".