Screening for poverty and intervening in a primary care setting: an acceptability and feasibility study
Bibliographic record
Abstract
BACKGROUND: A movement is emerging to encourage health providers and health organizations to take action on the social determinants of health. However, few evidence-based interventions exist. Digital tools have not been examined in depth. OBJECTIVE: To assess the acceptability and feasibility of integrating, within routine primary care, screening for poverty and an online tool that helps identify financial benefits. METHODS: The setting was a Community Health Centre serving a large number of low-income individuals in Toronto, Canada. Physicians were encouraged to use the tool at every possible encounter during a 1-month period. A link to the tool was easily accessible, and reminder emails were circulated regularly. This mixed-methods study used a combination of pre-intervention and post-intervention surveys, focus groups and interviews. RESULTS: Thirteen physicians participated (81.25% of all) and represented a range of genders and years in practice. Physicians reported a strong awareness of the importance of identifying poverty as a health concern, but low confidence in their ability to address poverty. The tool was used with 63 patients over a 1-month period. Although screening and intervening on poverty is logistically challenging in regular workflows, online tools could assist patients and health providers identify financial benefits quickly. Future interventions should include more robust follow-up. CONCLUSIONS: Our study contributes to the evidence based on addressing the social determinants of health in clinical settings. Future approaches could involve routine screening, engaging other members of the team in intervening and following up, and better integration with the electronic health record.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".