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Enregistrement W6910189660 · doi:10.3886/e182923v2-157288

Companion Files for Racial Differences in Patient Experience and Diabetes Management Outcomes among Reproductive-Age Women

2023· dataset· en· W6910189660 sur OpenAlexaff

Notice bibliographique

RevueICPSR Data Holdings · 2023
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésMedical Expenditure Panel SurveyHealth careDiabetes managementEthnic groupDiseaseDiabetes mellitusDisease managementMEDLINEType 2 diabetes

Résumé

récupéré en direct d'OpenAlex

The STATA do files for creating recodes and analyzing the data files will be provided in this folder for each paper once submitted for publication. Revised do files will be uploaded after the peer review process is complete if there are any changes to the files. <br><br>The data for this study are publicly available on the Medical Expenditure Panel Survey website (https://meps.ahrq.gov/mepsweb/). <br><br><b>Summary</b><br>Significant racial and ethnic disparities in cardiometabolic diseases, such as diabetes, underline entrenched health inequalities in the United States. Non-pregnant, non-Hispanic black women of reproductive age (18-45 years) are more likely to have diagnosed and undiagnosed diabetes, which increases their risk of maternal morbidity and mortality during the perinatal period. Adherence to disease management and monitoring during the preconception period is crucial, especially among non-Hispanic black women who are disproportionately impacted by maternal morbidity and mortality. Studies have shown positive patient experiences are associated with adherence to recommended medication and treatment, preventive care use, and self-rated health outcomes. There is a dearth of studies, however, examining the effects of patient experiences (and racial differences in patient experiences) on chronic disease management outcomes specifically among non-pregnant, reproductive-age women with diabetes. An understanding of these associations have important implications for maternal morbidity and mortality. The goal of this study is to use the Medical Expenditure Panel Survey datasets (2012-2017 longitudinal files) and robust statistical modeling techniques to investigate racial differences in patient experience among non-pregnant, reproductive-age women with diabetes and its relation to ratings of health care received, diabetes care self-efficacy, and diabetes care monitoring. This study provides important information for researchers, clinicians, and policy-makers. The research addresses the Maternal and Child Health Bureau (MCHB) Strategic Research Issue II: MCH services and systems of care efforts to eliminate health disparities and barriers to health care access for MCH populations. This study informs the development of equitable clinical patient-centered practices that promote optimal disease management among diverse women and reduce racial and ethnic disparities in maternal health outcomes. It also strengthens and expands MCH Services Block Grant National Performance and Population Priority Domain I: “Well-Woman Visits and Preconception/Interconception Health”. This study is expected to help determine whether positive patient experiences can improve women’s confidence in their abilities to manage their diabetes, and increase their likelihood of receiving recommended diabetes care during the preconception or interconception period. By elucidating the mechanisms by which promoting patient-centered diabetes care interventions during the preconception/interconception period might improve disease management, our study can inform practices and policies that contribute to the attainment of following Healthy People 2020 Maternal, Infant, and Child Health (MIC) Objectives: Increase the proportion of women delivering a live birth who received preconception care services and practiced key recommended preconception health behaviors (MICH-16); Reduce the rate of maternal illness and complications due to pregnancy (MICH-6); and Reduce the rate of maternal mortality (MICH-5). Furthermore, our study findings are expected identify the patient experiences that have the greatest impact on diabetes management outcomes, and can lead to policy changes for provider reimbursements for demonstrating quality patient-provider interactions. By providing insights into ways health care professionals can better communicate with this at-risk population, our study is relevant to the attainment of Healthy People 2020 Health Communication and Health Information Technology (HC/HIT) Objectives: Increase the proportion of persons who report that their health care providers have satisfactory communication skills (HC/HIT-2) and Increase the proportion of persons who report that their health care providers always involved them in decisions about their health care as much as they wanted (HC/HIT-3).

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,431
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0030,005
Intégrité de la recherche0,0000,000
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 Opus0,062
Tête enseignante GPT0,308
Écart entre enseignants0,246 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

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
Publié2023
Routes d'admission1
Résumé présentoui

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