Informing decision makers about public preferences for different modalities of cancer treatment in the Rhône–Alps region in France
Bibliographic record
Abstract
BACKGROUND: Alternative options to hospital care like home care or local health centers (LHCs) are being advocated. However, no study has measured citizens' preferences (who will finance these services via taxation) for these options. OBJECTIVES: We measured (i) citizens' preferences for these services, that is, respondents stated where they would like to get the treatment; (ii) the strength of their preference. METHODS: A computerized survey composed of (i) a decision aid to inform respondents about the three options; (ii) three scenarios, from light-to-heavy care, that respondents should rank from the most to the least preferred option of care. (iii) a contingent valuation survey (CVS) to assess how much respondents were willing to pay for their preferred option (except for hospital care if chosen, because it is the default option and free). (iv) a socio-demographic questionnaire. RESULTS: = 800). The heavier the care was, the more respondents preferred hospital care. Willingness to pay for additional taxation per household/month varied from €13.9 for light care in LHC to €19.1 for heavy home care. The small number of protesting respondents and outliers, and the close correlation between preferences, income, and WTP supports the validity of the CVS. CONCLUSION: In France, for cancer, not all citizens would prefer to be treated at home rather than in a hospital. Only less than a quarter would prefer LHC. These results show the mismatch between public health policies and the citizens' preferences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".