Factors influencing the recruitment and retention of primary health care nurses in rural and remote areas:An evidence- based approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background and aims: Over a quarter of the Australian population live rurally or remotely. As a <br/>result, many encounter multiple barriers to accessing healthcare and experience poorer health <br/>outcomes. The Australian government’s focus on the National Health Priority Areas has identified <br/>nurse-led models of care as a catalyst for increased access to care. Primary health care nurses are at <br/>the forefront of this work, transforming how care is delivered and addressing the unmet health needs <br/>of local communities. Given the ongoing challenges of recruiting and retaining the health workforce, it <br/>is vital to undertake research that examines factors supporting retention especially in rural and remote <br/>areas. The Australian Primary Health Care Nurses Association (APNA) is delivering and evaluating a <br/>Commonwealth funded program focused on implementing 37 nurse-led clinics across the country. <br/>The aim of this presentation is to highlight the early findings focused on a deeper understanding of the <br/>experiences of primary health care nurses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it