Confidence in receiving medical care when seriously ill: a seven‐country comparison of the impact of cost barriers
Bibliographic record
Abstract
OBJECTIVE: This paper examines how negative experiences with the health-care system create a lack of confidence in receiving medical care in seven countries: Australia, Canada, Germany, The Netherlands, New Zealand, the United Kingdom, and the United States. METHODS: The empirical analysis is based on data from the Commonwealth Fund International Health Policy Survey 2007, with nationally representative samples of adults aged 18 and over. For the analysis of the experience of cost barriers and confidence in receiving medical care, we conducted pairwise comparisons of group percentages as well as country-wise multivariate logistic regression models. RESULTS: Individuals who have experienced cost barriers show a significantly lower level of confidence in receiving safe and quality medical care than those who have not. This effect is most pronounced in the United States, where people who have foregone necessary treatment because of costs are four times as likely to lack confidence as individuals without the experience of cost barriers (adjusted odds ratio 4.00). In New Zealand, Germany, and Canada, individuals with the experience of cost barriers are twice as likely to report low confidence compared with those without this experience (adjusted odds ratios of 1.95, 2.19 and 2.24, respectively). In The Netherlands and UK, cost barriers are only a marginal phenomenon. CONCLUSIONS: The fact that the experience of financial barriers considerably lowers confidence indicates that financial incentives, such as private co-payments, have a negative effect on overall public support and therefore on the legitimacy of health-care systems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".