How to Better Integrate Social Determinants of Health into Primary Healthcare: Various Stakeholders’ Perspectives
Bibliographic record
Abstract
This paper aims to identify challenges and opportunities related to the integration of social determinants of health (SDH) into primary healthcare at an international symposium in Orford, Quebec, Canada. A descriptive qualitative approach was conducted. Three focus groups on different topics were led by international facilitators. Two research team members took notes during the focus groups. All the material was analyzed using a thematic analysis according to an inductive method. Many challenges were identified, leading to the identification of potential opportunities: integrate the concept of SDH in all phases of the training curriculum for health professionals to foster interprofessional and intersectoral collaboration and sociocultural skills; organize healthcare for better outreach to vulnerable populations; organize local and regional committees to develop management frameworks to produce and use territory-specific data; develop dashboards for primary healthcare providers describing the composition of their territory's population; work collaboratively, rallying primary healthcare providers, community organization delegates, patient partners, citizens, and municipality representatives around common projects. Discussions prompted new directions for further primary healthcare research, among which are building on best practices in the literature and in the field, and engaging various stakeholders in research, including vulnerable populations, while focusing on patient experience.
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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 | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".