A population profile of older adults with prescription encounters with nurse practitioners and family physicians in Ontario: a descriptive retrospective cohort study
Bibliographic record
Abstract
Background: Compared with patients in family physician practices, there is a poor understanding at a population level of patients in nurse practitioner practices. The study aim was to use Ontario administrative databases to identify the sociodemographic characteristics and comorbidities of patients aged 65 years and older who were prescribed medications by nurse practitioners and family physicians between 2000 and 2015. Methods: This population-based descriptive retrospective cohort study included patients 65 years of age and older with Ontario Health Insurance Plan eligibility and at least 1 prescription encounter with a nurse practitioner or family physician during the study period. Prescription identification of patients permitted their characterization by age, sex, geographical location, rurality, neighbourhood income and comorbidities. Patients were categorized into 3 provider groups on the basis of the percentage of prescription encounters with nurse practitioners versus family physicians. Results: In 2015, patients in the study cohort with prescriptions by nurse practitioners (n = 25 220) were younger than those with prescriptions by family physicians (40.3% were aged 65–69 yr) and they were more likely to be residents of low-income neighbourhoods (44.0% were in the lowest 2 neighbourhood income quintiles) and to be living outside of central Ontario. In contrast, patients who received prescriptions from family physicians (n = 1 952 904) tended to be older (26.8% were aged ≥ 80 yr), to have higher incomes (21.1% were in the highest neighbourhood income quintile) and to live in urban areas (86.5%). Mean Elixhauser Comorbidity Index scores were consistently lower among patients cared for by nurse practitioners than among those predominantly seen by family physicians (1.30 v. 2.04). The most prevalent conditions were hypertension and diabetes, regardless of provider. Interpretation: The patient characteristic with the highest variability between providers was geographic residence in the province. Elucidating patterns of care is critical for primary care policy and our results provide baseline data for future health care planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".