Validation of a standardised approach to collect sociodemographic and social needs data in Canadian primary care: cross-sectional study of the SPARK tool
Bibliographic record
Abstract
OBJECTIVE: This study validates the previously tested Screening for Poverty And Related social determinants to improve Knowledge of and access to resources ('SPARK Tool') against comparison questions from well-established national surveys (Post Survey Questionnaire (PSQ)) to inform the development of a standardised tool to collect patients' demographic and social needs data in healthcare. DESIGN: Cross-sectional study. SETTING: Pan-Canadian study of participants from four Canadian provinces (SK, MB, ON and NL). PARTICIPANTS: 192 participants were interviewed concurrently, completing both the SPARK tool and PSQ survey. MAIN OUTCOMES: Survey topics included demographics: language, immigration, race, disability, sex, gender identity, sexual orientation; and social needs: education, income, medication access, transportation, housing, social support and employment status. Concurrent validity was performed to assess agreement and correlation between SPARK and comparison questions at an individual level as well as within domain clusters. We report on Cohen's kappa measure of inter-rater reliability, Pearson correlation coefficient and Cramer's V to assess overall capture of needs in the SPARK and PSQ as well as within each domain. Agreement between the surveys was described using correct (true positive and true negative) and incorrect (false positive and false negative) classification. RESULTS: There was a moderate correlation between SPARK and PSQ (0.44, p<0.0001). SPARK correctly classified 71.4% of participants with or without a social need. There was strong agreement with most demographic questions. SPARK correctly classified 74.3% of participants as having financial insecurity. Clustering financial security questions had fewer false negatives (6.4%, n=11/171 vs 9.9%, n=17/171) and more false positives (19.3%, n=33/171 vs 11.1%, n=19/171) when compared with the question 'difficulty making ends meet'. When looking specifically at participants with high UCLA loneliness scores (>60), SPARK correctly classified 90.5% (n=176/191). CONCLUSIONS: SPARK provides a brief 15 min screening tool for primary care clinics to capture social and access needs. SPARK was able to correctly classify most participants within each domain. Related ongoing research is needed to further validate SPARK in a large representative sample and explore primary care implementation strategies to support integration.
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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 | 0.073 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".