Factors Associated With the Workforce Participation Intentions of Australian Primary Health Care Nurses and Midwives
Bibliographic record
Abstract
Introduction: Primary health care (PHC) is fundamental to supporting individual and community health and well-being. There is a need to better understand factors impacting on PHC nurses’ and midwives’ intentions to remain in PHC work. Methods: Data were obtained from the 2022 Australian Primary Health Care Nurses Association (APNA) Workforce Survey, conducted online in the final quarter of 2022. Logistic regression modelling was employed to identify explanatory factors of intention to remain working in PHC over the next 12 months (Model 1) and over the next 2–5 years (Model 2). Results: There were 3,749 valid survey responses. Key predictors determined included elements of rurality, stress at work and access to computer resources in Model 1 ( n = 2995), and years of nursing experience, being First Nations, working full-time or part-time, pay and conditions, and access to education and training in Model 2 ( n = 3,004). In both models, aspects of job satisfaction and working to full scope of practice were key predictors of intention to remain in PHC work. Conclusions: Key fixed and modifiable explanatory factors identified in this research point to the need for a range of local, organisational and broader-scale initiatives to support the ongoing recruitment and retention of nurses and midwives in PHC practice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".