Social and health determinants of wait times for primary care in Canada
Bibliographic record
Abstract
OBJECTIVE: To identify social and health determinants affecting wait times for primary care in Canada. DESIGN: Secondary analysis of national survey data. SETTING: Canada. PARTICIPANTS: Canadian Community Health Survey participants aged 18 years or older during the 2015, 2016, and 2019 cycles. MAIN OUTCOME MEASURES: Weighted summary statistics and a partial proportional odds regression model were used to assess relationships between patient-level social and health determinants and wait times for primary care (ranging from same-day appointments to waits of 1 month or longer). RESULTS: A weighted sample of 9,380,662 participants across the 3 survey cycles was used. Participants had a mean age of 49.7 years (standard deviation=17.5), 55.7% were female, and 74.7% were white. Approximately 57.4% of participants waited less than a week for care while 11.0% waited 1 month or longer. Factors associated with higher odds of waiting at least 1 month for a consultation were attaining only a high school diploma (adjusted odds ratio [aOR]=1.12, 95% CI 1.01 to 1.23) compared with having postsecondary education; identifying as Asian (aOR=1.42, 95% CI 1.20 to 1.67) or Black (aOR=1.57, 95% CI 1.16 to 2.13) compared with identifying as white; having "excellent" self-reported health status (aOR=1.10, 95% CI 1.02 to 1.19) compared with "good" self-reported health; lacking a regular primary care provider (aOR=1.47, 95% CI 1.27 to 1.71) compared with having a regular provider; or being classified as having multimorbidity (aOR=1.11, 95% CI 1.01 to 1.21) compared with having no chronic condition. CONCLUSION: Wait times for primary care were found to be associated with patient-level social and health determinants including race, education level, absence or presence of a regular health care provider, self-reported health status, and multimorbidity. Future research could investigate health system determinants of wait times to inform specific policy measures designed to reduce disparities in access to care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".