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Enregistrement W4394978470 · doi:10.1101/2024.04.18.24306035

The Validity and Reliability of Dichotomized Self-rated Health Under Different Cutpoints

2024· preprint· en· W4394978470 sur OpenAlexafffundabout
Charles Plante, Sharalynn Missiuna, Cory Neudorf

Notice bibliographique

RevuemedRxiv · 2024
Typepreprint
Langueen
DomaineSocial Sciences
ThématiqueHealth disparities and outcomes
Établissements canadiensUniversity of SaskatchewanSaskatchewan Health Authority
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésLikert scaleScale (ratio)Reliability (semiconductor)PsychologyPublic healthHealth careCommunity healthApplied psychologyMedical educationMedicineNursingGeographyPolitical scienceCartography

Résumé

récupéré en direct d'OpenAlex

Abstract Self-rated health is a widely used indicator of overall health status. It is most often reported on a Likert scale of three to five values in surveys. To facilitate presentation and interpretation, it is common practice to simplify the variable by dichotomizing it; however, there has been little documented reflection on how this should be done. This paper explores all four possible dichotomizations of self-reported health, taken from three years of the Canadian Community Health Survey and reported by a Likert scale. We evaluated each dichotomization stratified by sociodemographic variables. We use regression analysis to explore the validity and reliability of all four possible dichotomizations by mapping them to the Health Utility Index. We found that lower cutpoints of dichotomization capture more pronounced differences in health status and are more consistent across sociodemographic variables. However, higher cutpoints of dichotomization should be considered for small data sets. About the Research Department The Saskatchewan Health Authority Research Department leads collaborative research to enhance Saskatchewan’s health and healthcare. We provide diverse research services to SHA staff, clinicians, and team members, including surveys, study design, database development, statistical analysis, and assistance with research funding. We also spearhead our own research programs to strengthen research and analytic capability and learning within Saskatchewan’s health system. About the UPHN The Urban Public Health Network (UPHN) is a national organization established in 2004 which today includes the Medical Officers of Health in 24 of Canada’s large urban centres. Working collaboratively and with a collective voice, the network addresses public health issues that are common to urban populations. Research operations of the UPHN are conducted in partnership with the University of Saskatchewan. Disclaimer This working paper is for discussion and comment purposes. It has not been peer-reviewed nor been subject to review by Research Department staff or executives. Any opinions expressed in this paper are those of the author(s) and not those of the Saskatchewan Health Authority. Suggested Citation Charles Plante, Sharalynn Missiuna, and Cordell Neudorf. 2024. “The Validity and Reliability of Dichotomized Self-rated Health Under Different Cutpoints.” medRxiv. Extended Abstract Introduction Self-rated health is a widely used indicator of overall health status. It is most often reported on a Likert scale of three to five values in surveys. To facilitate presentation and interpretation, it is common practice to simplify the variable by dichotomizing it; however, little documented reflection has been done on how this should be done. Methods We use regression analysis to explore the validity and reliability of all four possible dichotomizations of self-reported health in the Canadian Community Health Survey in 2013-2015 by mapping them to a validated health measure: the Health Utility Index Mark 3 (HUI). We posit that more valid cutpoints in self-rated health are associated with larger changes in HUI. We posit further that more reliable cutpoints are associated with similar changes across sociodemographic variables, including age, sex, education, marital status, geography and income. We also provide descriptive statistics to contextualize our analysis. Results The greatest proportion of respondents reported having “very good” health, although the proportion of the population reporting “excellent” or “very good” health decreased with age. Similarly, Canadians tend to score highly in HUI. Our regression results suggest that HUI tends to be higher for younger, richer, married, educated and urban populations. However, these associations are muted as the cutpoint used to dichotomize self-reported health is raised. The model with the lowest cutpoint, distinguishing between poor health and all other health statuses, was associated with the greatest and most consistent negative changes in HUI among different sociodemographic groups. Conclusions Dichotomizing self-rated health using lower cutpoints captures more pronounced differences in health status measured by HUI and tends to capture more consistent differences across sociodemographic variables. That is, lower cutpoints produce more valid and reliable results. However, lower cutpoints isolate less commonly reported health levels and may lead to less accurate results in smaller populations. Key Points This article addresses the knowledge gap concerning the most accurate way to dichotomize self-rated health data reported using a Likert scale. This paper explores the validity and reliability of all four possible dichotomizations of self-reported health reported by a Likert scale. Lower cutpoints of dichotomization capture more pronounced differences in health status and are more consistent across sociodemographic variables. Higher cutpoints of dichotomization should be considered for small data sets.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,078
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,049
Tête enseignante GPT0,370
Écart entre enseignants0,320 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations12
Publié2024
Routes d'admission3
Résumé présentoui

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