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Enregistrement W4392209372 · doi:10.1097/nnr.0000000000000711

Health Misinformation and Nursing Science

2023· article· en· W4392209372 sur OpenAlexaff
Rita H. Pickler

Notice bibliographique

RevueNursing Research · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMisinformation and Its Impacts
Établissements canadiensChild, Adolescent and Family Mental Health
Organismes subventionnairesnon disponible
Mots-clésMisinformationNursingPsychologyNursing scienceMedicineComputer science

Résumé

récupéré en direct d'OpenAlex

In early 2023, we published a call for articles for a special issue of Nursing Research on innovative research designed to counteract health misinformation. We were looking forward to receiving many submissions on this important topic. Unfortunately, that did not happen. We had a few queries about the call, but those queries were not followed by articles. We had only a handful of submissions related to the call; none was accepted for publication. We did publish a few articles in 2023 that partly addressed health misinformation (i.e., Jones et al., 2023; Zha et al., 2023), but we are puzzled by the limited response to the call. Research about ways to provide accurate health information and about how health information is understood and used within different groups of people is clearly within the scope of nursing science. We know that researchers generally have found that healthcare providers, including nurses, do little to correct health misinformation, especially on social media (Bautista et al., 2021), although provider intent to correct misinformation is relatively high. Whether this lack of action is related to not knowing how to correct misinformation, an absence of organizational support to correct information posted on social media, or indifference to the problem is not known. This lack of action by healthcare providers to correct misinformation is one aspect of the misinformation phenomenon that requires researcher attention. More broadly, research is needed about how to distribute accurate health information while also correcting health misinformation. Health misinformation thrives in the absence of easily accessible, credible information; when there is limited or contradictory information; or when the information is emotionally conveyed (Sylvia Chou et al., 2020). Moreover, acceptance of misinformation may result from an inability to distinguish between accurate and inaccurate data, an unwillingness to make distinctions between accurate or inaccurate information, or both. There may also be uncertainty about a health situation or an information void, which increases the potential for rapid spread of misinformation (i.e., COVID and other recent epidemics). Uncertainty is generally a bad thing where health is concerned as persons may accept dangerous or erroneous information as true to fill knowledge gaps. At the same time, individuals may also fail to perceive a problem with the information they are using to guide health decisions, believing instead they are making good health decisions even when they are not (i.e., vaccine avoidance in highly educated parents). In addition, misinformation can spread more rapidly when there is societal division and in situations of distrust. This may result in persons accepting misinformation as true even if they have access to scientifically accurate information. Recipients may also accept misinformation as true because of historical or contextual legacies (Southwell et al., 2023). In other words, inaccurate and false health-related messages do not alone constitute misinformation (Krishna & Thompson, 2021); the public use of misinformation is integral to the problem. Nursing science has a clear mandate within the phenomenon of misinformation. In particular, nursing scientists have a long-standing interest in improving health literacy, defined as the ability to obtain, process, and understand basic health information and services to make appropriate health decisions (Parker et al., 2003). The spread of health misinformation would be severely limited if there was increased health literacy among all populations and especially among those populations for whom there are known health disparities. Counteracting health misinformation could result in a reduction of health disparities and thus, perhaps, an improvement in health equity, especially among populations vulnerable to misunderstanding. Health misinformation is a threat to public health and human well-being (Jackson et al., 2021). Research is needed to better understand how people are exposed to and affected by misinformation and how misinformation varies across populations. Research is also needed about effective strategies to prevent and address health misinformation and to improve persons' ability to make informed decisions about health and healthcare. It is particularly important to rigorously test the effectiveness of strategies for distributing accurate health information as well as the effectiveness of strategies to correct misinformation. Research about these topics and interventions to improve health literacy, including digital health literacy, are needed. These areas of research are within the scope and mandate of nursing science. Here at Nursing Research, we are open to receiving articles reporting this work so that we may learn and go forward together in our efforts to improve health outcomes for all.

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)0,081
score de la tête « metaresearch » (Gemma)0,204
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,081
Score d'incertitude au seuil0,426

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0810,204
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0110,006
Études des sciences et des technologies0,0100,062
Communication savante0,0220,027
Science ouverte0,0030,014
Intégrité de la recherche0,0150,018
Charge utile insuffisante (le modèle a refusé de juger)0,0120,002

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 Opus0,215
Tête enseignante GPT0,574
Écart entre enseignants0,359 · 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

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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