Assessing Priorities in a Statewide Cardiovascular and Diabetes Health Collaborative Based on the Results of a Needs Assessment: Cross-Sectional Survey Study
Notice bibliographique
Résumé
BACKGROUND: The Ohio Cardiovascular and Diabetes Health Collaborative (Cardi-OH) unites general and subspecialty medical staff at the 7 medical schools in Ohio with community and public health partnerships to improve cardiovascular and diabetes health outcomes and eliminate disparities in Ohio's Medicaid population. Although statewide collaboratives exist to address health improvements, few deploy needs assessments to inform their work. OBJECTIVE: Cardi-OH conducts an annual needs assessment to identify high-priority clinical topics, screening practices, policy changes for home monitoring devices and referrals, and preferences for the dissemination and implementation of evidence-based best practices. The results of the statewide needs assessment could also be used by others interested in disseminating best practices to primary care teams. METHODS: A cross-sectional survey was distributed electronically via REDCap (Research Electronic Data Capture; Vanderbilt University) to both Cardi-OH grant-funded and non-grant-funded members (ie, people who have engaged with Cardi-OH but are not funded by the grant). RESULTS: In total, 88% (103/117) of Cardi-OH grant-funded members and 8.14% (98/1204) of non-grant-funded members completed the needs assessment survey. Of these, 51.5% (53/103) of Cardi-OH grant-funded members and 47% (46/98) of non-grant-funded members provided direct clinical care. The top cardiovascular medicine and diabetes clinical topics for Cardi-OH grant-funded members (clinical and nonclinical) were lifestyle prescriptions (50/103, 48.5%), atypical diabetes (38/103, 36.9%), COVID-19 and cardiovascular disease (CVD; 38/103, 36.9%), and mental health and CVD (38/103, 36.9%). For non-grant-funded members, the top topics were lifestyle prescriptions (53/98, 54%), mental health and CVD (39/98, 40%), alcohol and CVD (27/98, 28%), and cardiovascular complications (27/98, 28%). Regarding social determinants of health, Cardi-OH grant-funded members prioritized 3 topics: weight bias and stigma (44/103, 42.7%), family-focused interventions (40/103, 38.8%), and adverse childhood events (37/103, 35.9%). Non-grant-funded members' choices were family-focused interventions (51/98, 52%), implicit bias (43/98, 44%), and adverse childhood events (39/98, 40%). Assessment of other risk factors for CVD and diabetes across grant- and non-grant-funded members revealed screening for social determinants of health in approximately 50% of patients in each practice, whereas some frequency of depression and substance abuse screening occurred in 80% to 90% of the patients. Access to best practice home monitoring devices was challenging, with 30% (16/53) and 41% (19/46) of clinical grant-funded and non-grant-funded members reporting challenges in obtaining home blood pressure monitoring devices and 68% (36/53) and 43% (20/46) reporting challenges with continuous glucose monitors. CONCLUSIONS: Cardi-OH grant- and non-grant-funded members shared the following high-priority topics: lifestyle prescriptions, CVD and mental health, family-focused interventions, alcohol and CVD, and adverse childhood experiences. Identifying high-priority educational topics and preferred delivery modalities for evidence-based materials is essential for ensuring that the dissemination of resources is practical and useful for providers.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».