Specialist care visits outside the hospital by South Australian older adults
Bibliographic record
Abstract
BACKGROUND: Limited access to specialist medical services is a major barrier to healthcare in rural areas. We compared rural-urban specialist doctor consultations outside hospital by older adults (≥ 60 years) across South Australia. METHODS: Cross-sectional data were available from the South Australia's Department of Health. The Modified Monash Model (MM1-7) of remoteness was used to categorize data into rural (MM 3-4), remote (MM5-7), and urban (MM1-MM2) of participants in urban and non-urban South Australia. The analysis was conducted on older adults (n = 20,522), self-reporting chronic physical and common mental health conditions. RESULTS: Specialist doctor consultation in the past 4 weeks was 14.6% in our sample. In multivariable analysis, increasing age (odds ratio 1.3, 95% CI: 1.2-1.4), higher education (odds ratio 1.5, 95% CI: 1.3-1.9), physical health conditions [diabetes (odds ratio 1.2, 95% CI: 1.1-1.3); cancer (odds ratio1.8, 95% CI: 1.7-2.0); heart disease (odds ratio 1.9, 95% CI: 1.6-2.1)], and common mental disorders [depression (odds ratio 1.3, 95% CI: 1.1-1.5); anxiety (odds ratio 1.4, 95% CI: 1.1-1.6)] were associated with higher specialist care use. Specialist care use among rural (odds ratio 0.8, 95% CI: 0.6-0.9), and remote (odds ratio 0.8, 95% CI: 0.7-0.9) older people was significantly lower than their urban counterparts after controlling for age, education, and chronic disease. CONCLUSION: Our findings demonstrate a disparity in the use of out of hospital specialist medical services between urban and non-urban 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".