Collecting social determinants data in Canadian primary care clinics: Lessons from the SPARK Implementation Study
Bibliographic record
Abstract
Context: The routine and systematic collection of demographic and social needs data remains uncommon in primary care clinics across Canada. Such data can create a foundation to integrate social interventions into clinical care, to identify health inequities, and to reorient health organizations to address the “upstream” factors that result in illness, disability and death seen “downstream”. Objective: To report on findings from a national mixed-methods study of implementing a tool for routinely collecting patients’ demographic and social needs data in primary care. Study Design and Analysis: We conducted a mixed methods implementation evaluation over a 6-month period. Setting: Researchers across Manitoba, Saskatchewan, Ontario, Nova Scotia and Newfoundland implemented the SPARK Tool in five primary care clinics. Instrument: The SPARK Tool includes questions about demographics (e.g. race, gender identity) and social needs (e.g. income, housing). Dataset: Data include SPARK Tool patient responses (n=>2500), patient and clerical staff feedback surveys, interviews with patients (n=~50) and clinic staff (n=~30), focus groups (n=~10) and implementation surveys (n=~40) with clinic leaders, providers, and clerical staff. Population Studied: Patients, clerical staff, and providers in primary care clinics using the SPARK Tool. Outcome Measures: Implementation outcomes such as number of surveys completed/declined and number of surveys completed on tablets in-clinic or online using an emailed survey link. Results: SPARK Tool completion rates were approximately 50%. Smaller clinics had a higher proportion of responses from tablets in-clinic compared to larger clinics, where most responses were completed using an emailed survey link. Patients, providers, clerical staff, and clinic leadership perceived the SPARK Tool to be acceptable for routine demographic and social needs data collection. A challenge was a lack of provider and staff time to explain the tool to patients. Patients and providers shared concerns about having sensitive social data in patients’ medical records. A benefit of implementation included providers learning about previously unknown social needs of some patients. Conclusions: The results highlight challenges and benefits of using a demographic and social needs data collection tool in primary care across Canada. These will guide efforts to scale up this data collection in Canadian primary care and beyond.
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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 | 0.142 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.002 | 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".