What stresses remote area nurses? Current knowledge and future action
Bibliographic record
Abstract
OBJECTIVE: Review and synthesise the literature identifying the stresses experienced by remote area nurses (RANs). Identify interventions implemented to address identified stresses. Explore the use of the job demands-resources (JD-R) model. METHODS: A comprehensive literature review was conducted using the meta-databases Ovid and Informit. SETTING: Remote Australian primary health care centres. RESULTS: The reported demands experienced by RANs can be grouped into four themes: (i) the remote context; (ii) workload and extended scope of practice; (iii) poor management; and (iv) violence in the workplace and community. In this high-demand, low-resource context, the JD-R model of occupational stress is particularly pertinent to examining occupational stress among RANs. The demands on RANs, such as the isolated geographical context, are immutable. However, there are key areas where resources can be enhanced to better meet the high level of need. These are: (i) adequate and appropriate education, training and orientation; (ii) appropriate funding of remote health services; and (iii) improved management practices and systems. CONCLUSION: There is a lack of empirical evidence relating to stresses experienced by RANs. The literature identifies some of the stresses experienced by RANs as unique to the remote context, while some are related to high demands coupled with a deficit of appropriate resources. Use of models, such as the JD-R model of occupational stress, might assist in identifying key areas where resources can be enhanced to better meet the high level of need and reduce RANs' levels of stress.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".