‘Change talk’ among physicians in small group learning communities: An ethnographic study
Bibliographic record
Abstract
INTRODUCTION: Physicians face uncertainties in complex clinical environments. Small group learning initiatives allow physicians to decipher new evidence and address challenges. This study aimed to understand how physicians in small learning groups discuss, interpret and assess new evidence-based information to make decisions for practice. METHODS: An ethnographic approach was used to collect data from observed discussions between practising family physicians (n = 15) that meet in small learning groups (n = 2). Physicians were members of a continuing professional development (CPD) programme that provides educational modules with clinical cases and evidence-based recommendations for best practice. Nine learning sessions were observed over 1 year. Field notes documenting the conversations were analysed using ethnographic observational dimensions and thematic content analysis. Observational data were supplemented with interviews (n = 9) and practice reflection documents (n = 7). A conceptual framework for 'change talk' was created. RESULTS: Observations elucidated the following: Facilitators played a significant role in leading the discussion by focusing on practice gaps. As group members shared approaches to clinical cases, baseline knowledge and practice experiences were revealed. Members made sense of new information by asking questions and sharing knowledge. They determined what information was useful and whether it applied to their practice. They reviewed evidence, tested algorithms, benchmarked themselves to best practice and consolidated knowledge before committing to practice change(s). Themes from interviews emphasised that sharing of practice experiences played an integral part in decisions to implement new knowledge, helped validate guideline recommendations and provided strategies for feasible practice changes. Documented practice reflections regarding decisions for practice change(s) overlapped with field notes. CONCLUSION: This study provides empirical data on how small groups of family physicians discuss evidence-based information and make decisions for clinical practice. A 'change talk' framework was created to illustrate the processes that occur when physicians interpret and assess new information to bridge gaps between current and best practices.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".