Perspectives of family physician educators on implementing shared decision making for preventive health care
Bibliographic record
Abstract
Context: Shared decision making (SDM) is often underutilised in clinical practice. In prior work, we observed varying views among resident physicians about the need for SDM. However, we know little about the views on implementing SDM in practice from the perspective of those who supervise and educate residents. Objective: To explore the views of family physician educators about the implementation of SDM in clinical practice, with a focus on the delivery of preventive health care. Study Design and Analysis: Qualitative descriptive study using a practical thematic analysis of data from individual online interviews. Population: We interviewed family physician educators based at three teaching units in Montreal. Using a purposive approach to recruitment, participants had to be practising family doctors who supervised family medicine residents in their clinics. Results: Nine female and six male family physician educators were interviewed, with practice experience ranging from 2 to 42 years (average 19 years). They spent 93% of their time in community-based clinical practice (range 66-100%) and supervised residents from 4 to 12 hours a week (average 9). We identified five overarching themes (with sub themes in italicised text), grouped into two categories: 1. Conceptual ideas about the nature of SDM and 2. Challenges that surround putting SDM into practice when delivering preventive health care. Themes in category #1 were: (1a) Participants held diverse and dynamic understandings of SDM (difficulty conceptualising what SDM is, understanding of SDM changes over time, SDM requires clinical equipoise). (1b) Participants identified why SDM is important (patient centred care). Themes in category #2 were: (2a) When to engage in SDM is influenced by multiple external factors (systemic factors, research-based evidence) as well as (2b) Patient factors (social factors, discordance or misalignment between doctor and patient on a specific decision, patient safety). (2c) Resources and strategies are needed to put SDM into action. Supporting quotes will be presented for each theme. Conclusion: In the context of delivering preventive health care, we found inconsistent conceptual understanding of SDM across physician educators in Family Medicine. As a result, putting SDM into practice faces multiple challenges.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".