IMPROVING MEDICAID ENROLLMENT AND POPULATION HEALTH:THREE PAPERS FOR STATES.
Notice bibliographique
Résumé
This dissertation consists of three research studies designed to assist states with improving the health of their populations. Due to states’ greatly-increased responsibilities for promoting, providing, and regulating their residents’ health insurance; their continuing responsibilities for public health promotion, prevention, and surveillance; and their growing understanding of the relationship between individual health and the well-being of communities, many states now see themselves as stewards of their populations’ health. Yet, states struggle to ensure that eligible individuals receive insurance. Plus, they lack robust systems to monitor the health of their populations. In addition, they face ongoing fiscal and staffing challenges that will make it difficult to satisfy these responsibilities in the foreseeable future. The first two studies utilize quantitative and data visualization techniques to describe state-level Medicaid and Children’s Health Insurance Program (CHIP) enrollment patterns and dynamics between 2000 and 2011 for the purposes of identifying policies and procedures to expedite eligibility determinations, renewals, and transfers and thereby improve program participation. Specifically, study one utilizes state administrative data and dummy variables representing eligibility policies and procedures to estimate the relationship between unemployment and enrollment during a period of significant economic and policy change. It finds that the Medicaid participation rate increases with the unemployment rate and with large expansions of eligibility criteria, such as an expansion to childless adults like that authorized under the Affordable Care Act (ACA). Study two, the first to demonstrate Medicaid enrollment seasonality, draws from a robust set of state-level administrative data to analyze month-to-month changes by eligibility category. The four eligibility categories—children, parents, aged, and disabled— show distinct and consistent enrollment patterns. Insights into these patterns can inform outreach efforts, as well as the development of eligibility policies and management strategies for preventing backlogs. The third study, which received Fulbright program support, draws lessons and recommendations for states for monitoring population health from a case study of population health monitoring in the Canadian province of Saskatchewan, a federal substate that shares many socioeconomic characteristics with its American counterparts and which has provided universal health insurance for over fifty years.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».