Perspectives of family physician educators on shared decision making in preventive health care: A Qualitative Descriptive Inquiry
Bibliographic record
Abstract
OBJECTIVE: To explore the views of family physician (FP) educators on shared decision making (SDM). METHODS: Qualitative descriptive study. Individual interviews were recorded with FPs in active practice who were also educators of Family Medicine residents. Data were analyzed following the phases of practical thematic analysis. RESULTS: 15 practicing FP educators in a clinic setting were interviewed; nine female and six male FPs with practice experience averaging 19 years. We identified five themes, which we then grouped in two major categories: (i) Conceptual ideas about SDM and (ii) Challenges in putting SDM into practice. In the conceptual idea category: (1) Participants held different understandings of SDM and did not have consensus about when SDM should be achieved in clinical practice (difficulty conceptualizing what SDM is, understanding of SDM changes over time, SDM requires clinical equipoise). (2) Participants identified why SDM is important (patient-centred care). Themes in the putting SDM into practice category (ii) were: (3) When to engage in SDM is influenced by multiple factors (system factors, research-based evidence) as well as (4) patient factors (social or contextual factors, discordance or misalignment between doctor and patient on a specific decision, patient safety). (5) Resources and strategies are needed to put SDM into action. CONCLUSION: An inconsistent understanding of SDM among FP educators, as well as several other challenges, helps explain why SDM has been difficult to implement in practice. PRACTICE IMPLICATIONS: Physician educators will appreciate how this study unveils challenges to enhancing resident training for the use of SDM in primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".