Universal Pharmacare and Contraceptive Dispensations Among Youth
Bibliographic record
Abstract
Importance: Previous studies have suggested that removing financial barriers to contraception could help reduce unintended pregnancy. Objective: To assess whether introduction of universal public funding for prescription contraception in Ontario (OHIP+) for individuals younger than 25 years and the amended program, which limited public funding to those without private insurance (OHIP-), is associated with changes in contraceptive dispensations. Design, Setting, and Participants: Interrupted time-series analyses were used to evaluate whether implementation of either policy was associated with changes in monthly contraceptives dispensed. The setting included a national database on contraceptives dispensed from retail pharmacies between September 2016 and February 2020; data analysis was performed from May 2022 to 2024. Participants included Ontario females aged 15 to 24 years to whom prescriptions were dispensed (intervention) vs controls: (1) Canadian females aged 15 to 24 years, excluding Ontario, and (2) Ontario females aged 25 to 49 years. Exposures: Implementation of free prescription contraception through OHIP+ (January 2018-March 2019) and OHIP- (April 2019-February 2020). Main Outcomes and Measures: Monthly dispensations of intrauterine devices (IUDs) and oral contraceptive pills (OCPs) per 1000 females overall and by area-level socioeconomic status (SES). Results: After OHIP+, there was an immediate level increase in IUDs dispensed to Ontario females aged 15 to 24 years (intervention) of 0.50 (95% CI, 0.15-0.84) vs 0.03 (95% CI, -0.26 to 0.32) in Canadian females aged 15 to 24 years-a relative increase of 0.48 (95% CI, 0.02-0.91). There was an immediate level increase in OCPs dispensed to Ontario females aged 15 to 24 years of 22.3 (95% CI, 14.8-29.8) vs 7.57 (95% CI, 3.07-12.1) in those aged 25 to 49 years-a relative increase of 14.8 (95% CI, 6.15-23.4). There were no statistically significant changes in monthly dispensation trends after OHIP+ and no statistically significant changes after OHIP-. In areas with lower SES, there was a significant increase in the level for IUDs of 0.64 (95% CI, 0.02-1.26) and for OCPs of 13.2 (95% CI, 1.33-25.0) after OHIP+, and a significant decrease in the level for IUDs of 0.82 (95% CI, -1.55 to -0.09) after OHIP- in Ontario vs Canadian females aged 15 to 24 years. No statistically significant changes in IUD or OCP dispensations were observed in areas with higher SES vs controls. Conclusions and Relevance: Results reveal that providing comprehensive and confidential access to prescription contraceptives was associated with increased dispensations among Ontario youth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".