Clinical teachers’ views on how teaching teams deliver and manage residency training
Bibliographic record
Abstract
BACKGROUND: Residents learn by working in a multidisciplinary context, in different locations, with many clinical teachers. Although clinical teachers are collectively responsible for residency training, little is known about the way teaching teams function. AIM: We conducted a qualitative study to explore clinical teachers' views on how teaching teams deliver residency training. METHOD: Data were collected during six focus group interviews in 2010. RESULTS: The analysis revealed seven teamwork themes: (1) clinical teachers were more passionate about clinical expertise than about knowledge of teaching and teamwork; (2) residents needed to be informed about clinical teachers' shared expectations; (3) the role of the programme director in the teaching team needed further clarification; (4) the main topics of discussion in teaching teams were resident performance and the division of teaching tasks; (5) the structural elements of the organisation of residency training were clear; (6) clinical teachers had difficulty giving and receiving feedback and (7) clinical teachers felt under pressure to be accountable for team performance to external parties. CONCLUSION: The clinical teachers did not consider teamwork to be of any great significance to residency training. Teachers' views of professionalism and their own experiences as residents may explain their non-teamwork directed attitude. Efforts to strengthen teamwork within teaching teams may impact positively on the quality of 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".