Implementing social interventions in primary care in Canada: A qualitative exploration of lessons learned from leaders in the field
Bibliographic record
Abstract
PURPOSE: Primary health care providers and practices are increasingly instituting direct interventions into social determinants of health and health inequities, but experiences of the leaders in these initiatives remain largely unexamined. METHODS: Sixteen semi-structured interviews with Canadian primary care leaders in developing and implementing social interventions were conducted to assess barriers, keys to success, and lessons learned from their work. RESULTS: Participants focused on practical approaches to establishing and maintaining social intervention programs and our analysis pointed to six major themes. A deep understanding of community needs, through data and client stories, forms a foundation for program development. Improving access to care is essential to ensuring programs reach those most marginalized. Client care spaces must be made safe as a first step to engagement. Intervention programs are strengthened by the involvement of patients, community members, health team staff, and partner agencies in their design. The impact and sustainability of these programs is enhanced by implementation partnerships with community members, community organizations, health team members, and government. Health providers and teams are more likely to assimilate simple, practical tools into practice. Finally, institutional change is key to establishing successful programs. CONCLUSION: Creativity, persistence, partnership, a deep understanding of community and individual social needs, and a willingness to overcome barriers underlie the implementation of successful social intervention programs in primary health care settings.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".