Not just trust: factors influencing learners’ attempts to perform technical skills on real patients
Bibliographic record
Abstract
CONTEXT: As part of their training, physicians are required to learn how to perform technical skills on patients. The previous literature reveals that this learning is complex and that many opportunities to perform these skills are not converted into attempts to do so by learners. This study sought to explore and understand this phenomenon better. METHODS: A multi-phased qualitative study including ethnographic observations, interviews and focus groups was conducted to explore the factors that influence technical skill learning. In a tertiary paediatric emergency department, staff physician preceptors, residents, nurses and respiratory therapists were observed in the delivery and teaching of technical skills over a 3-month period. A constant comparison methodology was used to analyse the data and to develop a constructivist grounded theory. RESULTS: We conducted 419 hours of observation, 18 interviews and four focus groups. We observed 287 instances of technical skills, of which 27.5% were attempted by residents. Thematic analysis identified 14 factors, grouped into three categories, which influenced whether residents attempted technical skills on real patients. Learner factors included resident initiative, perceived need for skill acquisition and competing priorities. Teacher factors consisted of competing priorities, interest in teaching, perceived need for residents to acquire skills, attributions about learners, assessments of competency, and trust. Environmental factors were competition from other learners, judgement that the patient was appropriate, buy-in from team members, consent from patient or caregivers, and physical environment constraints. CONCLUSIONS: Our findings suggest that neither the presence of a learner in a clinical environment nor the trust of the supervisor is sufficient to ensure the learner will attempt a technical skill. We characterise this phenomenon as representing a pool of opportunities to conduct technical skills on live patients that shrinks to a much smaller pool of technical skill attempts. Learners, teachers and educators can use this knowledge to maximise the number of attempts learners make to perform technical skills on real patients.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".