The Primary Healthcare Nurse Practitioner in Ontario: A Workforce Study
Bibliographic record
Abstract
The role of the primary healthcare nurse practitioner (NP-PHC) has a long history in Ontario. In this paper, we describe the evolution of the role with a focus on geographic distribution, a profile of client populations and the services provided by NP-PHCs. Comparisons will be made to findings from previous studies and reports on the NP-PHC role in Ontario. In 2004 and 2005, two-thirds of the nurse practitioners registered with the College of Nurses of Ontario responded to a descriptive self-reporting survey. The data collected revealed that NP-PHCs work throughout the healthcare system, including with underserviced and marginalized populations, in community health centres and in outpatient areas within acute care hospitals. They provide the entire spectrum of primary healthcare services. Barriers to fully enacting the role are related to restrictive legislation that limits NP prescribing and diagnosing, and the ability to work to full scope of practice in hospitals (for example, in emergency departments). Targeted funding has promoted the role throughout the province. However, inadequate and insecure pilot funding continues to be a concern. Findings from this study indicate that policy decisions to support the NP role in rural and remote areas have resulted in expansion of the role across the province. Yet, NPs perceive that legislation has lagged and inhibits their ability to meet patient and health systems needs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".