Barriers and Enablers to Direct Observation of Trainees’ Clinical Performance: A Qualitative Study Using the Theoretical Domains Framework
Bibliographic record
Abstract
PURPOSE: Direct observation is essential to assess and provide feedback to medical trainees. However, calls for its increased use in medical training persist as learners report that direct observation occurs infrequently. This study applied a theory-driven approach to systematically investigate barriers and enablers to direct observation in residency training. METHOD: From September 2016 to July 2017, semistructured interviews of faculty and residents at The Ottawa Hospital were conducted and analyzed. An interview guide based on the theoretical domains framework (TDF) was used to capture 14 domains that may influence direct observation. Interview transcripts were independently coded using direct content analysis, and specific beliefs were generated by grouping similar responses. Relevant domains were identified based on the frequencies of beliefs reported, presence of conflicting beliefs, and perceived influence on direct observation practices. RESULTS: Twenty-five interviews (12 residents, 13 faculty) were conducted, representing 10 specialties. Ten TDF domains were identified as influencing direct observation: knowledge, skills, beliefs about consequences, social/professional role and identity, intention, goals, memory/attention/decision processes, environmental context and resources, social influences, and behavioral regulation. Discord between faculty and resident intentions, coupled with social expectations that residents should be responsible for ensuring that observations occur, was identified as a key barrier. Additionally, competing demands identified across multiple TDF domains emerged as a pervasive theme. CONCLUSIONS: This study identified key barriers and enablers to direct observation. These influencing factors provide a basis for the development of potential strategies aimed at embedding direct observation as a routine pedagogical practice in residency training.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".