The Nurse Practitioner Workforce in Western Canada: A Cross-Sectional Practice Analysis Comparison
Bibliographic record
Abstract
Regular examination of health workforce data is essential given the pace of health system and legislative changes. Health workforce studies pertaining to nurse practitioner (NP) practice are needed to examine the gaps between work activities, policy, human resource supply, or for population needs. Jurisdictional comparison studies can provide essential information about NP practice for governments to respond to health workforce deficiencies or engage in service planning. In Canada, there is limited provincial-territorial jurisdictional NP workforce data to support health planning or policy change. This descriptive cross-sectional study was to examine the similarities and differences in practice patterns of Canadian NPs. In 2016 and 2017, an electronic survey was sent to all 852 registered NPs in three Canadian provinces, yielding a large convenience sample of 375 NP respondents. The results of this study underscore the value of NPs' extensive registered nurse expertize as well as their ability to serve diverse patient populations, work in varied healthcare settings, and provide care to medically complex patients. The study findings also show that NPs in all three jurisdictions work to their full scope of practice, in both rural and urban settings. This study is the first to compare NP workforce data across multiple Canadian jurisdictions simultaneously. Studies of this type are valuable tools for understanding the demographics, education, integration, and employment activities of NPs and can aid governments in addressing workforce 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".