Care for chronic illness in Australian general practice – focus groups of chronic disease self-help groups over 10 years: implications for chronic care systems reforms
Bibliographic record
Abstract
BACKGROUND: Chronic disease is a major global challenge. However, chronic illness and its care, when intruding into everyday life, has received less attention in Asia Pacific countries, including Australia, who are in the process of transitioning to chronic disease orientated health systems. AIM: The study aims to examine experiences of chronic illness before and after the introduction of Australian Medicare incentives for longer consultations and structured health assessments in general practice. METHODS: Self-help groups around the conditions of diabetes, epilepsy, asthma and cancer identified key informants to participate in 4 disease specific focus groups. Audio taped transcripts of the focus groups were coded using grounded theory methodology. Key themes and lesser themes identified using a process of saturation until the study questions on needs and experiences of care were addressed. Thematic comparisons were made across the 2002/3 and 1992/3 focus groups. FINDINGS: At times of chronic illness, there was need to find and then ensure access to 'the right GP'. The 'right GP or specialist' committed to an in-depth relationship of trust, personal rapport and understanding together with clinical and therapeutic competence. The 'right GP', the main specialist, the community nurse and the pharmacist were key providers, whose success depended on interprofessional communication. The need to trust and rely on care providers was balanced by the need for self-efficacy 'to be in control of disease and treatment' and 'to be your own case manager'. Changes in Medicare appeared to have little penetration into everyday perceptions of chronic illness burden or time and quality of GP care. Inequity of health system support for different disease groupings emerged. Diabetes, asthma and certain cancers, like breast cancer, had greater support, despite common experiences of disease burden, and a need for research and support programs. CONCLUSION: Core themes around chronic illness experience and care needs remained consistent over the 10 year period. Reforms did not appear to alleviate the burden of chronic illness across disease groups, yet some were more privileged than others. Thus in the future, chronic care reforms should build from greater understanding of the needs of people with chronic illness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".