The health and health preparation of long-term Australian travellers
Bibliographic record
Abstract
A growing number of Australians are travelling domestically for extended periods. This creates challenges in both continuity of health care and burdens on health services. This paper reports a cross-sectional survey aimed to explore the health needs and health planning of long-term travellers. In total, 316 respondents who had travelled for more than 3 months consecutively in the last year participated. Most respondents were retired (n=197; 62.3%); however, ages ranged from 26 to 89 years. Nearly half of the respondents or their travel companion had a long-term illness that affected their daily life (n=135; 42.7%). Nearly half of respondents visited a GP (n=133; 42.1%), nearly one-quarter visited an Emergency Department (n=72; 22.8%) and 19.9% (n=63) visited another health provider while travelling. The level of preparation around health while travelling varied between participants. This study highlights that long-term travellers have significant health needs and are likely to require health services during their extended travel. Additionally, it identifies that currently few strategies are used to plan for health care during travel. This raises issues for rural and remote health services in terms of both capacity and continuity of care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".