Reducing occupational stress among registered nurses in very remote Australia: A participatory action research approach
Bibliographic record
Abstract
Background Nurses in very remote areas of Australia (RANs), work in complex and isolated settings for which they are often inadequately prepared, and stress levels are high. This paper, based on the ‘Back from the edge' project, evaluates the development and implementation of an intervention to reduce and prevent the impact of occupational stress in the RAN workforce in the Northern territory. Methods The methods involved a combined participatory action research/organisational development model, involving seven steps, to develop and implement system changes within the (then) Northern Territory Department of Health and Families (NTDH&F). The development, implementation and evaluation was informed via information from participants collected during workshops and interviews. Pre and post surveys were undertaken to evaluate the study. Results Occupational stress interventions developed by the workgroups were categorised into four main groups: (1) remote context, (2) workload and scope of practice, (3) poor management, and (4) violence and safety concerns. The main interventions centred on promoting a well educated, stable workforce. There were very few measurable changes as a result of the interventions as many were not able to be implemented in the time period of the study, but implementation is continuing. Conclusion While the outcome evaluations showed few effects, the study through consensus approaches, provides a blueprint for reducing stress among remote area nurses and evidence which should inform policy and practice with respect to service delivery in remote areas.
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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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".