Characteristics of Nurse Practitioner Practice in Family Health Teams in Ontario, Canada
Bibliographic record
Abstract
Nurse practitioners (NPs) in Ontario work in a number of settings, including physician-led, interprofessional Family Health Teams (FHTs). However, many aspects of NP practice within the FHTs are unknown. Our study aimed to describe the characteristics of NP practice in FHTs and the relationships between NPs and physicians within this model. This cross-sectional descriptive study analyzed NP service and diagnostic code data collected for every NP patient encounter from 2012 to 2015. Encounter data were linked to health administrative data housed at the Institute for Clinical Evaluative Sciences to allow for comparison with physician service and diagnostic codes. Findings demonstrated that NPs saw patients across all age groups for one to more than five problems per encounter and that NPs handled both acute and episodic care and chronic disease management issues. Patients with chronic conditions had more encounters with physicians than with NPs. In addition, compared to physicians, NPs saw more female than male patients. Our findings provide a snapshot of NP practice in FHTs and may be useful in informing other practice models in Ontario, elsewhere in Canada, and internationally. More evidence is needed, however, to clarify the responsibilities of the NPs in collaborative relationships with physicians and to embed policies that will ensure that NPs work to their full potential. In addition, applying service coding to all health care providers in FHTs could enhance data on interprofessional teams and the individual clinicians that comprise them.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".