‘Sometimes the work just needs to be done’: socio‐cultural influences on direct observation in medical training
Bibliographic record
Abstract
CONTEXT: Direct observation promises to strengthen both coaching and assessment, and calls for its increased use in medical training abound. Despite its apparent potential, the uptake of direct observation in medical training remains surprisingly limited outside the formal assessment setting. The limited uptake of observation raises questions about cultural barriers to its use. In this study, we explore the influence of professional culture on the use of direct observation within medical training. METHODS: Using a constructivist grounded theory approach, we interviewed 22 residents or fellows (10 male, 12 female) about their experiences of being observed during training. Participants represented a range of specialties and training levels. Data collection and analysis were conducted iteratively. Themes were identified using constant comparative analysis. RESULTS: Observation was used selectively; specialties tended to observe the clinical acts that they valued most. Despite these differences, we found two cultural values that consistently challenged the ready implementation of direct observation across specialties: (i) autonomy in learning and (ii) efficiency in health care provision. Furthermore, we found that direct observation was a primarily learner-driven activity, which left learners caught in the middle, wanting observation but also wanting to appear independent and efficient. CONCLUSIONS: The cultural values of autonomy in learning and practice and efficiency in health care provision challenge the integration of direct observation into clinical training. Medical learners are often expected to ask for observation, but such requests are socially and culturally fraught, and likely to constrain the wider uptake of direct observation.
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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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".