An exploration of the experiences of Australian Grey Nomads travelling with chronic conditions
Bibliographic record
Abstract
Internationally, the population is ageing and people are living well, longer. In Australia, extended travelling has gained popularity among older Grey Nomads due to time and opportunity post retirement. However, there is limited research available focusing on older Australians' health while travelling. This paper reports the qualitative phase of a larger mixed-method project that explores the experience of Australian Grey Nomads travelling with chronic conditions. Eight Grey Nomads participated in telephone interviews. Data were analysed using inductive thematic analysis. Two themes emerged, namely: continuity of care while travelling and experts on the road. Participants described encountering a fragmented health system, with challenges regarding finding health services; a lack of shared medical records; and difficulties accessing regular medications. Despite these challenges, participants demonstrated health preparedness, an ability to accommodate health on the road, and were all travelling for their health. This study highlights key systems issues that challenge health care while travelling, and identifies opportunities for both usual practices and rural health services to enhance the care provided to this group.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".