Poverty screening implementation in a Canadian primary care clinic: acceptability and feasibility for patients and providers
Bibliographic record
Abstract
Context While poverty is a risk factor for many chronic conditions, when it is recognized by care providers social screening can be used to positively impact patients’ health. Although there has been Canadian research on this topic, there have been no such studies in New Brunswick (NB). Objective This study fills a knowledge gap by asking: What is the adherence, acceptability, and feasibility of poverty screening administered by providers to patients of an NB primary care clinic? Study Design and Analysis The study is a concurrent mixed-methods implementation study. The quantitative data was analyzed using descriptive statistics and the qualitative data using inductive thematic analysis. Setting The study was set at St. Joseph’s Primary Care Clinic in Saint John, NB, Canada in 2023. Population Studied The study collected data from family physicians, nurse practitioners, and adult patients of the clinic. Intervention/Instrument Using an NB-specific clinical poverty screening tool, poverty screening was conducted by providers with in-person adult patients over a one-month period. Data was collected from patients following their primary care visit using a survey and medical records, and from providers using a pre- and post-intervention survey, a focus group, and screening records. Outcome Measures Key outcome measures included screening adherence, screening acceptability among patients, and screening acceptability and feasibility among providers as well as willingness to continue screening. Results Screening data was collected from n = 467 patient visits, medical records were pulled from n = 246 patient charts, survey data was collected from n = 59 patients, and survey and focus group data was collected from n = 4 practitioners. Three quarters (78.5%) of eligible patients were screened for poverty, and of these a third (35.8%) screened positive. Nearly all missed screens were attributed to forgetfulness (94.4%). Of screened patients, 94.4% reported feeling “very comfortable” or “comfortable.” The post-intervention survey showed a shift in provider’s willingness to continue poverty screening. Conclusions Poverty screening was seen as acceptable among patients and providers. Providers, however, have feasibility concerns in a clinical setting with limited resources for social interventions. Results are being used to determine the potential for continued and expanded screening as well as to inform changes to screening protocols.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".