Nurses and midwives in north Australia: a survey of their long-term conditions and how they manage them
Bibliographic record
Abstract
Background/Aims: Although nurses and midwives are ageing, are in short supply, and they comprise the largest proportion of the health workforce, very little is known about how they manage any personal long-term conditions. This study aimed to identify the types and impacts of reported long-term conditions, and to identify strategies used to self-manage these conditions. Methods: A cross-sectional survey design was used. All nurses and midwives employed by the Health Service were sent a paper-based questionnaire, comprising six sections; 665 (30.9%) completed surveys were returned. The questionnaires were anonymous, and took no more than 25 minutes to complete; less if the nurse/midwife reported no long-term conditions. Results: Approximately two-thirds (n=401) reported having at least one long-term condition; musculoskeletal conditions were most frequently identified. More experienced nurses/midwives reported having more than one long-term condition. More than one quarter (n=107) identified conditions relating to mental health and wellbeing. Respondents were more likely to use personal than workplace-related strategies for managing their long-term conditions. Conclusion: Although this is a non-representative sample, it is evident that nurses and midwives struggle with their own long-term conditions. The lower uptake of employer-provided strategies needs to be examined to minimise the loss of nurses and midwives from the workforce. This study has informed a similar study being undertaken with doctors and health practitioners in the Health Service; a larger cohort study involving nurses and midwives across metropolitan, rural and remote areas is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".