Primary care utilization in people who experience imprisonment in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
Abstract Background Access to primary care is an important determinant of health, and data are sparse on primary care utilization for people who experience imprisonment. We aimed to describe primary care utilization for persons released from prison, and to compare utilization with the general population. Methods We linked correctional data for all persons released from provincial prison in Ontario, Canada in 2010 with health administrative data. We matched each person by age and sex with four general population controls. We compared primary care utilization rates using generalized estimating equations. We adjusted rate ratios for aggregated diagnosis groups, to explore this association independent of comorbidity. We examined the proportion of people using primary care using chi squared tests and time to first primary care visit post-release using the Kaplan-Meier method. Results Compared to the general population controls, the prison release group had significantly increased relative rates of primary care utilization: at 6.1 (95% CI 5.9-6.2) in prison, 3.7 (95% CI 3.6-3.8) in the week post-release and between 2.4 and 2.6 in the two years after prison release. All rate ratios remained significantly increased after adjusting for comorbidity. In the month after release, however, 66.3% of women and 75.5% of men did not access primary care. Conclusions Primary care utilization is high in prison and post-release for people who experience imprisonment in Ontario, Canada. Increased use is only partly explained by comorbidity. The majority of people do not access primary care in the month after prison release. Future research should identify reasons for increased use and interventions to improve care access for persons who are not accessing care post-release.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".