Improving Health Equity by Screening for Poverty: A Survey of Family Physician Screening Behaviors and Perceptions in Toronto, Canada
Bibliographic record
Abstract
Purpose: Given the importance of socioeconomic status in both directly and indirectly influencing one's health, “poverty screening” by family physicians (FPs) may be one viable option to improve patient health. However, rates of screening for poverty are low, and reported barriers to screening are numerous. This study sought to collate and investigate reasons for refraining from screening among FPs, many of whom had opted into a Targeted Poverty Screening (TPS) Program, to be able to enhance uptake of the intervention. The TPS Program is a “targeted screening and referral process,” whereby medical charts of adult patients residing in “deprived neighborhoods,” as determined by postal code, were flagged for screening for FPs who elected to partake in the program. Methods: A survey containing 15 questions was developed through an iterative process with pilot-testing by faculty physicians. The survey was administered to FPs registered in the North York Family Health Team (NYFHT) using Qualtrics© research software. Results: Half of the respondents (n=19/38; 50%) indicated that they enrolled in the TPS program. Irrespective of enrollment in the TPS Program, the majority of respondents (n=31/38; 81.6%) stated that they elect to screen their patients for poverty using the evidence-based question of “do you have difficulty making ends meet at the end of the month?.” Among those not enrolled in the program, 84.2% (n=16/19) of respondents indicated that they screened their patients for poverty and 15.8% (n=3/19) indicated they did not. Among respondents who said they did not screen (n=7/38; 18.4%), the reasons for not screening patients were as follows: forgot (n=2; 28.6%); time constraints/feel uncomfortable asking (n=1; 14.3%); and “feel I know patients well” (n=1; 14.3%). For the remaining respondents, a nurse or locum did the screening as part of a periodic health review (i.e., patient was screened, but not by the FP completing the survey (n=3). Conclusion: This study yielded numerous insights, such as barriers faced by FPs in undertaking poverty screening that differs from the literature. The findings suggest that (1) barriers faced by FPs in poverty screening can be mitigated, (2) there is a need to integrate screening into routines and normalize the activity, and (3) there is a need for enhanced training to support patients of lower socioeconomic status.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".