Why We Don’t Need “Unmet Needs”! On the Concepts of Unmet Need and Severity in Health-Care Priority Setting
Bibliographic record
Abstract
In health care priority setting different criteria are used to reflect the relevant values that should guide decision-making. During recent years there has been a development of value frameworks implying the use of multiple criteria, a development that has not been accompanied by a structured conceptual and normative analysis of how different criteria relate to each other and to underlying normative considerations. Examples of such criteria are unmet need and severity. In this article these crucial criteria are conceptually clarified and analyzed in relation to each other. We argue that disease-severity and condition-severity should be distinguished and we find the latter concept better reflects underlying normative values. We further argue that unmet need does not fulfil an independent and relevant role in relation to condition-severity except for in some limited situations when having to distinguish between conditions of equal severity (and where other features also equals each other).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".