Patient value: Perspectives from the advocacy community
Bibliographic record
Abstract
All health-care systems are under financial pressure and many have therefore developed value frameworks to assist decision making regarding access to treatment. Unfortunately, many frameworks simply reflect the clinically focused values held by health-care professionals rather than outcomes that also matter to patients. It is difficult to define one single homogeneous set of patient values as these are shaped by social, religious and cultural factors, and health-care environment, as well as many factors such as age, gender, education, family and friends and personal finances. Instead of focusing on an aggregated set of values, frameworks should attempt to incorporate the broader range of outcomes that patients may regard as more relevant. Patient advocates are well placed to advise assessment bodies on how particular therapies will impact the patient population under consideration and should be closely involved in developing value frameworks. In this paper, a group of patient advocates explore the varying definitions of patient value and make positive recommendations for working together to strengthen the patient voice in this area. The authors call on framework developers, the patient advocacy and research communities, the health-care industry and decision-makers to undertake specific actions to ensure patient value is included in current and future value frameworks. This is justified on compassionate and economic grounds: better health outcomes result when patients receive treatment tailored to individual needs. Paying attention to the patient perspective also results in better use of resources-a goal that should appeal to all stakeholders.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.018 |
| 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".