Bibliographic record
Abstract
New health technologies are being developed, while public healthcare resources are limited. This situation makes prioritizing in the healthcare sector inevitable.\nHealth benefit, resource use and severity are the priority criteria on which the allocation of healthcare resources in Norway should be based. The quality-adjusted life-year (QALY) is used to operationalize these criteria. The QALY provides a combined measure of life length and health-related quality of life (HRQoL). This thesis addresses methodological challenges when measuring and valuing health for QALY calculations.\nInsights from psychometrics and health economics have been applied to help explain why different values get assigned to the same health state when using different instruments.\nQualitative research methods have been used to explore to what extent a patient group with mobility impairments can describe their mobility-related health status with currently used instruments. The findings indicate that not all instruments allow all respondent groups to represent their health states adequately.\nQuantitative research methods have been applied to achieve the following:\n- replicate the original 15D valuation method,\n- compare Finnish and Norwegian 15D health state values,\n- suggest a new 15D valuation method that reflects respondent’s preferences more adequately.\nThe new 15D valuation method has been used to estimate the first value algorithm based on preferences of the general Norwegian population. This allows decision makers to incorporate Norwegian preferences in the allocation of health care resources.
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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 | 0.588 | 0.737 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".