Paper 4: a systematic review on the use of logic models and frameworks for methodological conduct of evidence synthesis
Notice bibliographique
Résumé
OBJECTIVES: To identify evidence syntheses of health interventions addressing racial health equity reporting the use of equity-focused frameworks and logic models. STUDY DESIGN AND SETTING: The search strategy included three sources; a search of three bibliographic databases to identify systematic reviews assessing interventions to improve racial health equity, semistructured interviews with diverse group and a targeted organization website searches (eg, National Institute of Health, United States Preventive Services Task Force) to identify relevant logic models and frameworks. The searches were conducted between January 1, 2020, and January 25, 2023. We used a qualitative approach to identify and describe key characteristics of equity-focused logic models and frameworks used in evidence syntheses. RESULTS: Of the 153 racial health equity-focused evidence syntheses identified, two explicitly used logic models to describe the intervention mechanism. We identified seven existing health equity frameworks from semistructured interviews and electronic search of key websites that were categorized by stated purpose as providing guidance for 1) research, 2) health policy, 3) digital health-care solutions, and 4) clinical preventive services. Two out of seven frameworks included guidance on integrating frameworks or logic models in evidence synthesis while the majority provided contextual information on how to define or consider race or racism as a structural determinant of health. CONCLUSION: There is limited use of logic models and frameworks in evidence syntheses addressing racial health equity. There is a need for more applied frameworks providing guidance for framing, conducting and interpreting findings of evidence syntheses addressing racial health equity. PLAIN LANGUAGE SUMMARY: The goal of this study was to find reviews of health programs that focus on improving racial health equity, and to see if they used special frameworks or models designed to address equity. To do this, we searched three major research databases, conducted interviews with a diverse group of people, and looked at relevant organization websites (like the National Institute of Health and the World Health Organization) between January 2020 and January 2023. We used a qualitative approach to study the key features of these equity-focused frameworks and models. We found 153 reviews focused on racial health equity, but only two of them used logic models to explain the intervention. From interviews and website searches, we identified seven existing health equity frameworks. These were grouped into four categories: research, health policy, digital health care, and clinical preventive services. Only two of these frameworks provided advice on how to use them in evidence reviews, while most focused on understanding how race and racism impact health as a social factor. In conclusion, there is limited use of frameworks and models in reviews about racial health equity. More practical frameworks are needed to help guide the research and interpretation of these reviews.
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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 | 0,413 | 0,683 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,018 |
| Bibliométrie | 0,020 | 0,016 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,015 | 0,015 |
| Science ouverte | 0,006 | 0,010 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».