Health Disparities and Converging Epidemics in Jail Populations: Protocol for a Mixed-Methods Study
Notice bibliographique
Résumé
BACKGROUND: Incarcerated populations have increased in the last 20 years and >12 million individuals cycle in and out of jails each year. Previous research has predominately focused on the prison population. However, a substantial gap exists in understanding the health, well-being, and health care utilization patterns in jail populations. OBJECTIVE: This pilot study has 5 main objectives: (1) define recidivists of the jail system, individuals characterized by high incarceration rates; (2) describe and compare the demographic and clinical characteristics of incarcerated individuals; (3) identify jail-associated health disparities; (4) estimate associations between incarceration and health; and (5) describe model patterns in health care and jail utilization. METHODS: The project has two processes-a secondary data analysis and primary data collection-which includes a cross-sectional health survey and biological sample collection to investigate infectious disease characteristics of the jail population. This protocol contains pilot elements in four areas: (1) instrument validity and reliability; (2) individual item assessment; (3) proof of concept of content and database accessibility; and (4) pilot test of the "honest broker" system. Secondary data analysis includes the analysis of 6 distinct databases, each covered by a formal memorandum of agreement between Northern Arizona University and the designated institution: (1) the Superior Court of Arizona Public Case Finder database; (2) North Country Health Care; (3) Health Choice Integrated Care; (4) Criminal Justice Information Services; (5) Correctional Electronic Medical Records; and (6) iLEADS. We will perform data integration processes using an automated honest broker design. We will administer a cross-sectional health survey, which includes questions about health status, health history, health care utilization, substance use practices, physical activity, adverse childhood events, and behavioral health, among 200 Coconino County Detention Facility inmates. Concurrent with the survey administration, we will collect Methicillin-resistant and Methicillin-sensitive Staphylococcus aureus (samples from the nose) and dental microbiome (Streptococcus sobrinus and Streptococcus mutans samples from the mouth) from consenting participants. RESULTS: To date, we have permission to link data across acquired databases. We have initiated data transfer, protection, and initial assessment of the 6 secondary databases. Of 199 inmates consented and enrolled, we have permission from 97.0% (193/199) to access and link electronic medical and incarceration records to their survey responses, and 95.0% (189/199) of interviewed inmates have given nasal and buccal swabs for analysis of S. aureus and the dental microbiome. CONCLUSIONS: This study is designed to increase the understanding of health needs and health care utilization patterns among jail populations, with a special emphasis on frequently incarcerated individuals. Our findings will help identify intervention points throughout the criminal justice and health care systems to improve health and reduce health disparities among jail inmates. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/10337.
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 | 0,012 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».