Are guidelines for measurement of quality of life contrary to patient‐centred care?
Bibliographic record
Abstract
AIMS: A discussion of how quality-adjusted life years are used to inform resource allocation decisions and highlight how assumptions underpinning the measurement of quality of life are contrary to the principles of patient-centred care. BACKGROUND: Cost-effectiveness analyses (CEAs) can provide influential guidance for health resource allocation, particularly in the context of a budget-constrained public health insurance plan. Most national economic guideline bodies recommend that quality-adjusted life year weights for CEA be elicited indirectly (public preferences). This has potentially important implications for healthcare provision and research, as it discounts the ability of a person experiencing an illness to describe how it affects their quality of life. DESIGN: Discussion paper. DATA SOURCES: Guidelines for the conduct of health economic evaluations, influential methodological and theoretical texts, and a review of PubMed conducted in April 2017. IMPLICATIONS FOR NURSING: Nurses are increasingly interested in leveraging methods from health economics to aid in decision-making and advocacy. In this analysis, we highlight how taken-for-granted approaches to the measurement of quality of life may discount the experience of patients and lead to decisions that are contrary to the principles of patient-centred care. Nurses conducting or reading research using these methods should consider whether the approach used to measure the quality of life are appropriate for the population under consideration. CONCLUSION: Since patient and public health preferences can differ in both magnitude and direction, guideline bodies should re-evaluate their partiality for public preferences in the reference case.
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 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 | 0.300 | 0.592 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".