‘Health is their heart, their legs, their back’: understanding ageing well in ethnically diverse older men in rural Australia
Bibliographic record
Abstract
ABSTRACT Older men from ethnic minority communities living in a regional town in Australia were identified by a government-funded peak advocacy body as failing to access local health and support services and, more broadly, being at risk of not ageing well. A qualitative study was undertaken to explore the health and wellbeing of ethnic minority men growing older in a rural community, and to identify the barriers they faced in accessing appropriate services from a range of different perspectives. Individual interviews were conducted with key informants (service providers and community leaders), followed by focus groups with older men from four ethnic minority communities. The men in this study showed signs that they were at risk of poor mental and physical health, and experienced significant barriers to accessing health and support services. Furthermore, environmental, technological, social and economic changes have brought challenges for the older men as they age. Despite these challenges, this study demonstrated how work, family and ethnic identity was integral to the lives of these older men, and was, in many ways, a resource. Key informants' perspectives mostly confirmed the experiences of the older men in this study. The discrepancies in their views about the extent of health-promoting behaviour indicate some key areas for future health intervention, services and research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".