The reflecting team: an innovative approach for teaching clinical skills to family practice residents.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This paper provides a description and evaluation of the reflecting team approach as a teaching method for family practice residents. We have used the reflecting team approach in our longitudinal behavioral health program for 6 years. Our purpose in using this approach is to 1) teach listening and interviewing skills, 2) teach systems-oriented psychosocial interventions, and 3) provide behavioral health consultations for patients. METHODS: A five-item, self-administered, open-ended questionnaire evaluating the reflecting team approach was administered to a sample of family practice residents. RESULTS: Completed questionnaires were received from 18 of the 22 family practice residents participating in the longitudinal behavioral health program (a response rate of 82%). Responses to the questionnaire items indicated that the residents understood the purpose of the reflecting team approach and felt that they had acquired a variety of clinical skills from the approach, including listening and interviewing skills, positive reframing of patients' problems, how to give positive feedback to promote behavioral change, and increased knowledge of psychosocial assessment procedures and treatment methods. CONCLUSIONS: The residents' responses to the questionnaire items indicated that they perceived the reflecting team approach to be a practical and useful method for learning a variety of clinical skills.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".