Collecting data on key social determinants of health in primary care: a multi-site implementation evaluation
Bibliographic record
Abstract
Context: Social data that is collected in a standardized fashion can be used to improve individual patient care by uncovering and then addressing social needs, to stimulate the development of new integrated health and social programs, and be used to identify inequities across an organization. This data can also support system- and policy-level planning to identify health inequities at the population-level and support policy change. Objective: To report implementation outcomes using a standardized approach and tool in a multi-site study of routine and systematic demographic and social needs data collection in primary care settings. Study Design and Analysis: Between Sept 2022-Oct 2023 we implemented the SPARK Tool at five primary care clinics. We collected implementation outcomes and used surveys to assess the perspectives of patients, clerical staff and providers. Setting: We collected data at five primary care clinics in five Canadian provinces (Saskatchewan, Manitoba, Ontario, Nova Scotia, and Newfoundland and Labrador). Instrument: SPARK Tool, which includes 18 demographic and social needs questions Dataset: number of SPARK Tool surveys completed by patients (n=2063), patient feedback surveys (n=1368), clinic description and readiness checklists (n=5), provider and staff implementation surveys (n=36) and online training evaluation surveys (n=33) Population Studied: patients, clerical staff, and providers who had used or completed the SPARK Tool Outcome Measures: Implementation outcome measures including acceptability, adoption, feasibility, penetration, cost, appropriateness, fidelity, sustainability. Results: SPARK Tool completion rates varied significantly (9.4%-48%), indicating moderate penetration. The SPARK Tool was highly acceptable, with 90.5% of patients agreeing that the tool was clear and easy to complete and 84.5% having a positive experience. 58.1% reported being comfortable answering the questions. Clerical staff and provider 96.7% finding the SPARK Tool to be useful and 81.8% reporting a positive experience with using it. Conclusions: The results highlighted positive acceptability, feasibility, and the adoption of the SPARK Tool in diverse primary care clinics, as well as practical insight into implementing demographic and social needs data collection. Levels of penetration, patient comfort, and ease-of-use could be improved when using the tool at other sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.189 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".