Contextual factors in clinical decision making: national survey of Canadian family physicians.
Bibliographic record
Abstract
OBJECTIVE: Many factors are at play in the process of clinical decision making, but to date, the interaction of these factors has not been well understood. Such information could have important implications for teaching and promoting evidence-based medicine (EBM) in primary care. This study was designed to explore the relationship between physician-related variables and management of patient-related contextual factors in clinical decision making. A secondary objective was to examine the extent to which this relationship varies by type of clinical decision. DESIGN: Cross-sectional randomized postal survey of 1134 Canadian primary care physicians stratified by age, sex, and practice location. Nonrespondents were sent reminders at 4 weeks and again at 8 weeks; at 12 weeks, all remaining nonrespondents were mailed replacement copies of the questionnaire. SETTING: Family practices in Canada. PARTICIPANTS: Of the final sample of 431 family physicians, 52% were men, 63% practised in urban locations, and 71% were in group practice. MAIN OUTCOME MEASURES: Self-reported likelihood of considering various contextual factors during the course of clinical decision making. RESULTS: Despite the three follow-up mailings, the final response rate was 42%; however, nonrespondents did not differ significantly from respondents on three important demographic factors: age, sex, and practice location. Using multinomial logistic regression analysis, the data showed that female family physicians and practitioners less strongly identified with EBM were more likely to consider contextual factors in clinical decision making. The effect was more obvious for ordering tests than for decisions about treatment. CONCLUSION: The evolving model of EBM should consider important physician-related variables in clinical decision making. Our data indicate that physicians' sex and identification with the tenets of EBM influence management of contextual factors. These results have important implications because they indicate that clinicians strongly identified with the EBM model of clinical practice are less sensitive to context, which might be an obstacle to efforts to integrate patient values and clinical circumstances into patient-centred care. We believe these findings support continued development of the model of "context-sensitive medicine" previously proposed as an alternative to EBM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".