Factors associated with successful chronic disease treatment plans for older Australians: Implications for rural and Indigenous Australians
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with having a successful treatment plan for managing chronic conditions. DESIGN: Secondary analysis of the Commonwealth Fund's 2014 International Health Policy Survey. SETTING: Australia 2014. PARTICIPANTS: A total of 3310 Australian adults over 55 years old. MAIN OUTCOME MEASURES: Whether respondents: (i) had a treatment plan for their chronic condition; and (ii) believed that the plan was helpful in managing their condition. METHODS: We used multiple logistic regressions to assess the association between individual factors (age, income, remoteness, Australian Aboriginal or Torres Strait Islander status) and patient reports of the outcomes of interest. RESULTS: Most respondents reported having a treatment plan for their chronic condition(s); the majority reported that it was helpful in managing their health. Treatment plan provision was associated with age over 75 years, above-average income, Australian Aboriginal or Torres Strait Islander status and multiple chronic conditions. Plans were less likely for residents of outer regional and remote areas. Indigenous respondents were far less likely than non-Indigenous respondents to report that their treatment plan helped a lot. Respondents with providers who 'always' explained things were far more likely to say that a treatment plan helped. CONCLUSION: While the patient-provider relationship influenced the perceived success of treatment plans, inequities in treatment plan provision seemed linked with rurality and income. The higher frequency of treatment plans for Indigenous respondents might reflect access to Australian Aboriginal or Torres Strait Islander health checks, while the plan's perceived lack of efficacy suggests a gap in cultural acceptability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".