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Enregistrement W4210847602 · doi:10.1097/01.acm.0000803760.28371.94

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

2021· article· en· W4210847602 sur OpenAlexaff

Notice bibliographique

RevueAcademic Medicine · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealthcare cost, quality, practices
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesnon disponible
Mots-clésWaltzMEDLINE

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,018
score de la tête « metaresearch » (Gemma)0,110
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil0,166

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,110
Méta-épidémiologie (sens strict)0,0020,003
Méta-épidémiologie (sens large)0,0030,004
Bibliométrie0,0020,003
Études des sciences et des technologies0,0230,020
Communication savante0,0150,014
Science ouverte0,0110,010
Intégrité de la recherche0,1900,172
Charge utile insuffisante (le modèle a refusé de juger)0,0140,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,755
Tête enseignante GPT0,617
Écart entre enseignants0,138 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentnon

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