MétaCan
Menu
Back to cohort
Record W2242720477

Health Gain Maximization - An Ethically Defendable Criterion for Decisions on How to Allocate Health Care Resources?

2007· article· en· W2242720477 on OpenAlexaff
Jürgen John

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMaximizationValue (mathematics)Health careUtilitarianismEconomicsEconomic JusticeMicroeconomicsNormativePositive economicsActuarial scienceSocial psychologyPsychologySociologyPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

Rationale: In the health economic literature, many statements on the allocation of health care resources can be found showing an appreciation of the two criteria of efficiency and justice or fairness as two values which are to weighed against each other in case of conflict. Objective: This paper provides a critique of this model which is based on various arguments challenging the position that health gain maximization may be considered an ethically defendable value. Methodology: First, by returning to the survival lottery proposed by John Harris (1975), I will demonstrate that an ethically unbounded application of the health gain maximization rule may come into conflict with deeply rooted ethical norms such as the strict obligation not to kill innocent persons. Secondly, even an ethically controlled utilitarian position is confronted with John Taurek's no-worse-claim (Taurek 1977). In essence, Taurek argues that if life and death are at stake, it is the loss or gain to individuals that matters, not the loss or gain of individuals. As the sum of losses or gains resulting from aggregating individual losses or gains is nobody's loss or gain, interpersonally aggregated (dis)utilities of individuals do not have any moral relevance. Thus, maximization of health gain achievable with a given amount of resources cannot be regarded as an ethical principle on which a normative ranking of alternative allocations of resources could be based. Thirdly, one way of reconstructing our intuition of efficiency as a value might be to derive the principle of health gain maximization from a virtual social contract reflecting the societal preferences over the allocation of health care resources. In extreme cases of deadly decisions one could expect an ex-ante consent to the maximization rule, as its application would maximize everybody's probability to survive. In the case of less dramatic choices, however,it is doubtful whether a general agreement with a maximization rule can be assumed, as well. Particularly for QALY maximization which is the reference model of standard economic evaluation in health care, there is enough empirical evidence against this assumption. In a recent systematic review of studies reporting empirical data on public preferences, Dolan et al. (2005) demonstrate that the literature clearly suggests that QALY maximization is descriptively flawed: Contrary to the implications of QALY maximization, the social value of health gain is no linear function of changes in quality and length of life, is dependent of the age and other characteristics (e.g., having children or not) of the recipient, is affected by inequalities in health, and is dependent of how a fixed health gain is distributed. Thus, one cannot reasonable argue that QALY maximization can be justified by an implicit ex-ante consent. Result: In sum, theoretical as well as empirical arguments suggest that health gain maximization cannot be considered to be an ethically well-founded moral value. Conclusion: The normative importance of health gain maximization in the search for the best allocation of health care resources remains unclear.

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.048
Scholarly communication0.0080.010
Open science0.0030.006
Research integrity0.0140.009
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.054
GPT teacher head0.457
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicGlobal Health Care IssuesFrench-language works237,207