The Top 5 Minor Ailments and Pharmacist Management in Ontario: Attachment and Primary Care
Bibliographic record
Abstract
Context: Pharmacists represent the 3rd largest healthcare profession in Canada, after nurses and physicians, with 46,699 licensed to practice in 2022. Minor ailments (MAs) are health conditions that can be managed with low prescribed treatment and/or self-care strategies. In Ontario, Canada, pharmacists were given authority to deliver service for 19 MAs as of October 1, 2023. Objective: This study aims to identify and describe the characteristics of recipients of the five MAs most commonly managed by pharmacists. Setting: Ontario, Canada Study Design and Analysis: This study used linked health administrative data, including pharmacist billing for MA services, patient demographics, and physician and hospital billing data, collected for 15.4 million residents in Ontario. Logistic regression analysis was conducted for the delivery of specific MA services prescribed from January 1 to December 31, 2023, by primary care attachment. Models were adjusted for key recipient demographics and heatlcare utilization. Results: In 2023, 547,673 (3.6%) Ontario residents received at least one MA service, with the top 5 conditions being urinary tract infections (199,282 [36.4%]), conjunctivitis (153,021 [27.9%]), herpes labialis (46,659 [8.5%]), allergic rhinitis (41,521 [7.6%]), and atopic dermatitis/eczema/allergic contact dermatitis (39,365 [7.2%]). Comp ared to all other Ontario residents, MA service recipients were more likely to be attached to a primary care provider (odds ratio [95% confidence interval]: 1.92 [1.90-1.93]), be female (2.65 [2.63-2.67]), live in a higher income neighbourhood: 1.36 [1.35-1.37]), have a comorbidity (2.78 [2.76-2.91]) and use other pharmacy services (MedsCheck: 1.57 [1.55-1.58]; flu/COVID-19 vaccination: 1.69 [1.68-1.70]). In adjusted analyses, receipt of MA service remained significantly associated with primary care attachment (1.39 [1.37-1.40]). In adjusted analyses for each of the top MA services, recipients were significantly more likely to be attached with a primary care clinician: UTIs (1.41 [1.39-1.43]), conjunctivitis (1.69 [1.65-1.72]), Conclusion: MA prescribing by pharmacists was used by a small proportion of the population in the first year after introduction with more than half of visits relating to two ailments: UTIs and conjunctivitis. People living in higher income neighbourhoods and those with a primary care clinician were more likely to use MA services raising issues of equity and fragmentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".