An examination of retention factors among registered practical nurses in north-eastern Ontario, Canada
Bibliographic record
Abstract
Introduction: Literature from the past two decades has presented an insufficient amount of research conducted on the nursing practice environments of registered practical nurses (RPNs).The objective of this article was to investigate the barriers and facilitators to sustaining the nursing workforce in north-eastern Ontario (NEO), Canada.In particular, retention factors for RPNs were examined.Methods: This cross-sectional research used a self-administered questionnaire.Home addresses of RPNs working in NEO were obtained from the College of Nurses of Ontario (CNO).Following a modified Dillman approach with two mail-outs, survey packages were sent to a random sample of RPNs (N=1337) within the NEO region.Logistic regression analyses were used to determine intent to stay (ITS) in relation to the following factor categories: demographic, and job and career satisfaction.Results: Completed questionnaires were received from 506 respondents (37.8% response rate).The likeliness of ITS in the RPNs' current position for the next 5 years among nurses aged 46-56 years were greater than RPNs in the other age groups.Furthermore, the lifestyle of NEO, internal staff development, working in nursing for 14-22.5 years, and working less than 1 hour of overtime per week were factors associated with the intention to stay.Conclusions: Having an understanding of the work environment may contribute to recruitment and retention strategy development.The results of this study may assist with addressing the nursing shortage in rural and northern areas through improved retention strategies of RPNs.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".