Nurse Practitioner Intent to Leave: A Grounded Theory Study
Bibliographic record
Abstract
Aim: The aim of this grounded theory study was to better understand primary healthcare NP job satisfaction and intent to leave. Background: Nurse practitioner (NP) job satisfaction has been the focus of a variety of studies, however, NP retention as represented by intent to leave, while important, has received less attention within the literature. Methods: A constructivist grounded theory study was conducted in Northern Ontario, Canada. Twenty rural and urban primary healthcare NPs (NP-PHCs) were interviewed. Transcripts were analyzed to identify core categories related to primary healthcare NP-PHC intent to remain in current employment versus intending to leave. Findings: This theory identifies features contributing to intent to leave across urban and rural locations. Adequate remuneration, a provincial government pension, good relationships with management and an extended benefits program were associated with less interest in leaving. Key features of new positions among those considering leaving their current employment included adequate remuneration, additional extended benefits of employment, distance from home, and distance among different practice sites. Some respondents intending to leave identified that they would be seeking work outside of full time NP-PHC practice. Conclusions: This grounded theory study has served to clarify the key concepts related to intent to leave among this employee population. Concepts found to be associated with decreased intent to leave among Northern Ontario primary healthcare NPs were different from those identified within the current literature. Therefore, the findings of this study could more accurately inform initiatives to retain NP-PHCs within both rural and urban practice settings across Northern Ontario.
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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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".