Australian rural, remote and urban community nurses' health promotion role and function
Bibliographic record
Abstract
Community nurses have often been 'touted' as potential major contributors to health promotion. Critical literature, however, often states that this has not been the case. Furthermore, most studies examining nurses' role and function have occurred mainly in hospital settings. This is a sequential mixed-methods study of two groups of community nurses from a Sydney urban area (n = 100) and from rural and remote areas (n = 49) within New South Wales, Australia. A piloted questionnaire survey was developed based on the five action areas of the Ottawa Charter for Health Promotion. Following this, 10 qualitative interviews were conducted for both groups, plus a focus group to support or refute survey results. Findings showed that rural and remote nurses had more positive attitudes towards health promotion and its clinical implementation. Survey and interview data confirmed that urban community nurses had a narrower focus on caring for individuals rather than groups, agreeing that time constraints impacted on their limited health promotion role. There was agreement about lack of resources (material and people) to update health promotion knowledge and skills. Rural and remote nurses were more likely to have limited educational opportunities. All nurses undertook more development of personal skills (DPS, health education) than any other action area. The findings highlight the need for more education and resources for community nurses to assist their understanding of health promotion concepts. It is hoped that community nurse leaders will collectively become more effective health promoters and contribute to healthy reform in primary health care sectors.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".