Area deprivation and attachment to a general practitioner through centralized waiting lists: a cross-sectional study in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Access to primary healthcare is an important social determinant of health and having a regular general practitioner (GP) has been shown to improve access. In Canada, socio-economically disadvantaged patients are more likely to be unattached (i.e. not have a regular GP). In the province of Quebec, where over 30% of the population is unattached, centralized waiting lists were implemented to help patients find a GP. Our objectives were to examine the association between social and material deprivation and 1) likelihood of attachment, and 2) wait time for attachment to a GP through centralized waiting lists. METHODS: A cross-sectional study was conducted in five local health networks in Quebec, Canada, using clinical administrative data of patients attached to a GP between June 2013 and May 2015 (n = 24, 958 patients) and patients remaining on the waiting list as of May 2015 (n = 49, 901), using clinical administrative data. Social and material area deprivation indexes were used as proxies for patients' socio-economic status. Multiple regressions were carried out to assess the association between deprivation indexes and 1) likelihood of attachment to a GP and 2) wait time for attachment. Analyses controlled for sex, age, local health network and variables related to health needs. RESULTS: Patients from materially medium, disadvantaged and very disadvantaged areas were underrepresented on the centralized waiting lists, while patients from socially disadvantaged and very disadvantaged areas were overrepresented. Patients from very materially advantaged and advantaged areas were less likely to be attached to a GP than patients from very disadvantaged areas. With the exception of patients from socially disadvantaged areas, all other categories of social deprivation were more likely to be attached to a GP compared to patients from very disadvantaged areas. We found a pro-rich gradient in wait time for attachment to a GP, with patients from more materially advantaged areas waiting less than those from disadvantaged areas. CONCLUSION: Our findings suggest that there are socio-economic inequities in attachment to a GP through centralized waiting lists. Policy makers should take these findings into consideration to adjust centralized waiting list processes to avoid further exacerbation of health inequities.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".