Bibliographic record
Abstract
The Nurse Practitioner (NP) role has emerged to meet the growing need for access to primary health care (PHC). To ensure that the NP role is sustainable and provides access to PHC for Ontarians, there is a need to examine NP work environments and NP responses to their work environments, such as retention. Currently, factors influencing the retention of primary health care NPs in Ontario are not well known. As Ontario continues to invest a large amount of health care spending in emerging PHC organizational structures to enable access to primary care, health human resource strategies for NP retention are essential. However, there is a paucity of literature on NP work, work environments, and their impact on important NP and health care system outcomes, including retention. The primary objective of this study was to investigate the underlying factors influencing retention of NPs in PHC in Ontario. The primary research question was: what are the effects of individual NP characteristics, NP practice characteristics, and NP organizational characteristics on PHC nurse practitioner intent to remain employed (ITR) in Ontario? A non-experimental descriptive cross-sectional survey design was implemented. Self-administered surveys were mailed to primary health care NPs in Ontario with follow-up using a modified Dillman approach. Two hundred and seventy-three surveys were completed resulting in a useable response rate of 52.9%. A hypothesized model of the underlying factors influencing ITR of NPs in PHC in Ontario was tested using multiple regression. Regression results indicate that administration-NP relations, work-life balance, and family situation significantly explained 14% of the variance in primary health care NP intent to remain employed in Ontario (p
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".