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Commentary on “The Father-Daughter Dinner Dance: A Waltz With Ethics and COVID-19”

2021· article· en· W4210847602 on OpenAlexaff

Bibliographic record

VenueAcademic Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsWaltzMEDLINE

Abstract

fetched live from OpenAlex

This story—“The Father-Daughter Dinner Dance: A Waltz With Ethics and COVID-19”—is based on conversations I have had with friends, family, and colleagues about the allocation of medical resources during the COVID-19 pandemic. The characters and conversation in this story are all fictional. Although there is a strong public health effort to flatten the curve of infected individuals with COVID-19 over a longer period of time so that the health care system does not become overwhelmed, it is very likely that this pandemic will cause medical shortages. We are already hearing about a lack of personal protective equipment, ICU beds, and lifesaving technologies such as ventilators.1 During medical shortages, decision makers turn to bioethicists for advice on how to prioritize difficult decisions in an ethical and systematic manner. It is well noted that utilitarian approaches to decision making during times of scarce resources in health care are deemed acceptable.2 The basic criterion of utilitarianism as formulated by Jeremy Bentham in 1789 is the greatest happiness for the greatest number.3 In health care, it is desirable to save the greatest number of lives or the most life-years by giving priority to individuals likely to survive the longest after a given treatment.4 One of the most influential medical journals recently published an article that offered 6 recommendations for the fair allocation of scarce medical resources using a utilitarian perspective.1 Utilitarian approaches do not take into account that some health care professionals discriminate against certain groups of people. Although most health professionals are not likely to intentionally discriminate against their patients, they may hold negative attitudes that could cause their patients to feel disrespected and misunderstood.5 Research has demonstrated how some negative attitudes toward certain stigmatized groups are more acceptable than others. For example, negative attitudes toward people with mental illness or obesity are more acceptable than explicit racist or sexist beliefs.6,7 Negative attitudes become harmful stereotypes that create systemic barriers to health equity and result in biased clinical decision making in diagnosis and treatment of marginalized groups. In light of the knowledge we have about the negative health effects of discrimination on marginalized groups, it is frightening to think how a utilitarian approach could create life-threatening disadvantages during discussions of the fair allocation of scarce resources. This story highlights how social positions and identities intersect to create an environment of bias and discrimination against certain members of society.8 The daughter is a young, educated woman. She does not seem to have a chronic illness. She has completed a philosophy degree, but there is some pressure on universities to eliminate the humanities in favor of degrees deemed more productive. We do not have information on the father’s educational attainment, but we know that he has a blue-collar job and that he has a mental illness. The father and daughter’s social identities interact with the concept of ageism, which usually occurs against older adults; in this case, though, ageism is shown to affect younger adults depending on their health status. The mother, who comes into the story briefly, brings issues of gender and body image as intersections that combine with mental health to highlight further injustice and inequity. The purpose of creating a story on such a timely and complicated topic is to encourage the reader to think about how injustice toward one group can end up perpetuating systems of inequities toward other groups of people, especially when utilitarian approaches to lifesaving resource allocation are left unquestioned.

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)0.018
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.190
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.110
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.003
Science and technology studies0.0230.020
Scholarly communication0.0150.014
Open science0.0110.010
Research integrity0.1900.172
Insufficient payload (model declined to judge)0.0140.005

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 head0.755
GPT teacher head0.617
Teacher spread0.138 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2021
Admission routes1
Has abstractno

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