Health workers who ask about social determinants of health are more likely to report helping patients: Mixed-methods study.
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of implementing a clinical decision aid called the CLEAR Toolkit that helps front-line health workers ask their patients about social determinants of health, refer to local support resources, and advocate for wider social change. DESIGN: A mixed-methods study using quantitative (online self-completed questionnaires) and qualitative (in-depth interviews, focus groups, and key informant interviews) methods. SETTING: A large, university-affiliated family medicine teaching centre in Montreal, Que, serving one of the most ethnically diverse populations in Canada. PARTICIPANTS: Fifty family doctors and allied health workers responded to the online survey (response rate of 50.0%), 15 completed in-depth interviews, 14 joined 1 of 2 focus groups, and 3 senior administrators participated in key informant interviews. METHODS: Our multimethod approach included an online survey of front-line health workers to assess current practices and collect feedback on the tool kit; in-depth interviews to understand why they consider certain patients to be more vulnerable and how to help such patients; focus groups to explore barriers to asking about social determinants of health; and key informant interviews with high-level administrators to identify organizational levers for changing practice. MAIN FINDINGS: = .003). CONCLUSION: While health workers recognize the importance of social determinants, many are unsure how to ask about these often sensitive issues or where to refer patients. The CLEAR Toolkit can be easily adapted to local contexts to help front-line health workers initiate dialogue around social challenges and better support patients in clinical practice.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".