Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore how trust was constructed between surgeons and residents in the operating room. BACKGROUND: Entrustment is increasingly being used as a key element to assess trainees' competence in the clinical workplace. However, the cognitive process involved in the formulation of surgical trust remains poorly understood. METHODS: In semistructured interviews, 9 general surgeons discussed their experiences in making entrustment decisions during laparoscopic cholecystectomy. Template analysis methodology was employed to develop an explanatory model. RESULTS: Surgeons described the construction of trust as a stepwise process taking place before, during, and after the procedure. The main steps were as follows: (1) an initial propensity to trust based on the perceived risk of the case and trustworthiness of the resident; (2) a decision to initiate trust in the resident to begin the surgery; (3) close observation of preliminary steps; (4) an evolving decision based on whether the surgery is "on-track" or "off-track"; (5) intervention if the surgery was "off-track" (withdrawal of trust); (6) re-evaluation of trust for future cases. The main reasons described for withdrawing trust were: inability to follow instructions, failure to progress, and unsafe manoeuvres. CONCLUSIONS: This study showed that surgical trust is constructed through an iterative process involving gathering and valuing of information, decision-making, close observation, and supervisory intervention. There were strong underlying themes of control and responsibility, and trust was noted to increase over time and over repeated observations. The model presented here may be useful in improving judgements on competence in the surgical workplace.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| 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".