Members of Minority and Underserved Communities Set Priorities for Health Research
Notice bibliographique
Résumé
Policy Points Engaging and involving underrepresented communities when setting research priorities could make the scientific research agenda more equitable, more just, and more responsive to their needs and values. Groups and individuals from minority and underserved communities strongly prioritized child health and mental health research, often choosing to invest at the highest possible level. Groups consisting of predominantly Native American or Arab American participants invested in culture and beliefs research at the highest level, while many groups did not select it at all. The priority given to culture and beliefs research by these groups illustrates the importance of paying special attention to unique preferences, and not just commonly held views, when getting public input on spending priorities for research. CONTEXT: A major contributor to health disparities is the relative lack of resources-including resources for science-allocated to address the health problems of those with disproportionately greater needs. Engaging and involving underrepresented communities in setting research priorities could make the scientific research agenda more equitable, more just, and more responsive to their needs and values. We engaged minority and underserved communities in informed deliberations and report here their priorities for health research. METHODS: Academic-community partnerships adapted the simulation exercise CHAT for setting health research priorities. We had participants from minority and medically underserved communities (47 groups, n = 519) throughout Michigan deliberate about health research priorities, and we used surveys and CHAT software to collect the demographic characteristics and priorities selected by individuals and groups. FINDINGS: The participants ranged in age (18 to 88), included more women than men, and were overrepresented by minority groups. Nearly all the deliberating groups selected child health and mental health research (93.6% and 95.7%), and most invested at the highest level. Aging, access, promote health, healthy environment, and what causes disease were also prioritized by groups. Research on mental health and child health were high priorities for individuals both before and after group deliberations. Access was the only category more likely to be selected by individuals after group deliberation (77.0 vs 84.0%, OR = 1.63, p = .005), while improve research, health policy, and culture and beliefs were less likely to be selected after group deliberations (all, p < .001). However, the level of investment in many categories changed after the group deliberations. Participants identifying as Black/African American were less likely to prioritize mental health research, and those of Other race were more likely to prioritize culture and beliefs research. CONCLUSIONS: Minority and medically underserved communities overwhelmingly prioritized mental health and child health research in informed deliberations about spending priorities.
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 | 0,046 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,015 | 0,004 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,019 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,002 |
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 ».