Advancing Health Equity Through Primary Care: Protocol for the Spread, Scale, and Multimethod Developmental Evaluation of the Deep End Canada Network (Preprint)
Bibliographic record
Abstract
BACKGROUND The social determinants of health are “the conditions in which people are born, grow, live, work and age,” such as housing, employment, and race. Canadian primary health care organizations are increasingly looking for ways to systematically and routinely collect demographic and social needs data from patients to increase appropriate and responsive care by attending to the social determinants of health. The SPARK (screening for poverty and related determinants to improve knowledge of and links to local resources) tool is a standardized tool for use in primary health care settings that was developed, pilot-tested, refined, and validated in primary health care clinics across Canada. General Practitioners at the Deep End is a network of primary health care general practitioners working in the 100 most socioeconomically deprived areas in Scotland with the goal of connecting practitioners and advocating for better training, policies, and resources. Primary Health Care at the Deep End Canada or Soins primaires en milieux défavorisés Canada (Deep End Canada) was launched in June 2024 to offer guidance and resources to the growing number of primary health care teams interested in collecting and using demographic and social needs data. OBJECTIVE Our primary objective is to spread the sustainable implementation of the SPARK tool to systematically and routinely collect and use demographic and social needs data to respond to the social determinants of health in 20 to 25 primary health care organizations across Canada through the creation of Deep End Canada. METHODS Deep End Canada is a network of primary health care teams, including health professionals, researchers, and patient partners, working in areas with high rates of poverty. This project will use a multimethod developmental evaluation approach guided by the reach, effectiveness, adoption, implementation, and maintenance framework. Web-based surveys will capture the reach and adoption of demographic and social needs data collection, and network activities will be evaluated using qualitative analysis of focus groups, interviews, and meetings. RESULTS The study was funded in February 2022. Recruitment to the network commenced in June 2024 and included 10 organizations as of submission of the manuscript. Web-based survey data collection commenced in September 2024. Implementation of the network will be assessed from June 2024 to December 2025, with expected findings available in June 2026 and published in the fall of 2026. CONCLUSIONS Efforts to systematically and routinely collect and use demographic and social needs data and address structural issues in Canadian primary health care have grown significantly in the last 10 years. This increased commitment involves using data to develop evidence-based interventions that can reduce health inequities and address local community needs at the individual, organizational, and policy levels. This study will demonstrate how the Deep End Canada network contributes to achieving those aims by fostering collaboration among practitioners to promote health equity and tackle the structural and social determinants of health. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/75732
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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 | 0.203 | 0.167 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.111 | 0.017 |
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".