Health Systems and Sustainability: Doctors and Consumers Differ on Threats and Solutions
Bibliographic record
Abstract
BACKGROUND: Healthcare systems face the problem of insufficient resources to meet the needs of ageing populations and increasing demands for access to new treatments. It is unclear whether doctors and consumers agree on the main challenges to health system sustainability. METHODOLOGY: We conducted a mail survey of Australian doctors (specialists and general practitioners) and a computer assisted telephone interview (CATI) of consumers to determine their views on contributors to increasing health care costs, rationing of services and involvement in health resource allocation decisions. Differences in responses are reported as odds ratios (OR) and 99% confidence intervals (CI). RESULTS: Of 2948 doctors, 1139 (38.6%) responded; 533 of 826 consumers responded (64.5% response). Doctors were more concerned than consumers with the effects of an ageing population (OR 3.0; 99% CI 1.7, 5.4), and costs of new drugs and technologies (OR 5.1; CI 3.3, 8.0), but less likely to consider pharmaceutical promotional activities as a cost driver (OR 0.29, CI 0.22, 0.39). Doctors were more likely than consumers to view 'community demand' for new technologies as a major cost driver, (OR 1.6; 1.2, 2.2), but less likely to attribute increased costs to patients failing to take responsibility for their own health (OR 0.35; 0.24, 0.49). Like doctors, the majority of consumers saw a need for public consultation in decisions about funding for new treatments. CONCLUSIONS: Australian doctors and consumers hold different views on the sustainability of the healthcare system, and a number of key issues relating to costs, cost drivers, roles and responsibilities. Doctors recognise their dual responsibility to patients and society, see an important role for physicians in influencing resource allocation, and acknowledge their lack of skills in assessing treatments of marginal value. Consumers recognise cost pressures on the health system, but express willingness to be involved in health care decision making.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".