The effect of socioeconomic status on access to primary care: an audit study
Bibliographic record
Abstract
BACKGROUND: Health care office staff and providers may discriminate against people of low socioeconomic status, even in the absence of economic incentives to do so. We sought to determine whether socioeconomic status affects the response a patient receives when seeking a primary care appointment. METHODS: In a single unannounced telephone call to a random sample of family physicians and general practices (n = 375) in Toronto, Ontario, a male and a female researcher each played the role of a patient seeking a primary care physician. Callers followed a script suggesting either high (i.e., bank employee transferred to the city) or low (i.e., recipient of social assistance) socioeconomic status, and either the presence or absence of chronic health conditions (diabetes and low back pain). We randomized the characteristics of the caller for each office. Our primary outcome was whether the caller was offered an appointment. RESULTS: The proportion of calls resulting in an appointment being offered was significantly higher when the callers presented themselves as having high socioeconomic status than when they presented as having low socioeconomic status (22.6% v.14.3%, p = 0.04) and when the callers stated the presence of chronic health conditions than when they did not (23.5% v. 12.8%, p = 0.008). In a model adjusted for all independent variables significant at a p value of 0.10 or less (presence of chronic health conditions, time since graduation from medical school and membership in the College of Family Physicians of Canada), high socioeconomic status was associated with an odds ratio of 1.78 (95% confidence interval 1.02-3.08) for the offer of an appointment. Socioeconomic status and chronic health conditions had independent effects on the likelihood of obtaining an appointment. INTERPRETATION: Within a universal health insurance system in which physician reimbursement is unaffected by patients' socioeconomic status, people presenting themselves as having high socioeconomic status received preferential access to primary care over those presenting themselves as having low socioeconomic status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".