An Integrated Care Approach to Health Equity Data Collection and Use
Notice bibliographique
Résumé
Background: Inequities in healthcare have consistently been demonstrated to impact the health of patient populations. The East Toronto Health Partners (ETHP), a collection of more than 00 cross-sectional care delivery organizations in Ontario, Canada, have committed to addressing health inequities. In this study, we sought to understand how organizations within the ETHP define health equity and equity-seeking populations, how they currently collect and utilize health equity data, and their recommendations for system-wide collaboration to share and use the collected data to address the needs of the population they serve. This evaluation is a first step to equity-informed healthcare decision-making, to reduce inequities in care delivery and planning. Approach: In this exploratory qualitative descriptive study, we conducted 60- to 90-minute semi-structured interviews with ETHP anchor-partner decision-makers who were responsible for decision-making around the collection and use of health equity data in their respective organizations. Participants were purposively sampled and identified through snowball sampling. Concurrent content analysis was performed. Results: Study participants (n=6), from different organizations, included ETHP leaders holding various positions such as Director, CEO, Vice President, Board Chair, Quality Officer, Physicians and others. Seven unique equity data collection tools were identified. Some common data collected are language, birth country, ethnic/racial background, disability status, gender identity, sexual orientation and family income, among others. The interviews revealed a lack of common definition among ETHP partners; additionally, while there is an acknowledgement that equity data is beyond sociodemographic information, the current practice is limited to the collection of sociodemographic data in most settings. Currently, a scattered, top-down approach (directed by Ontario Health) is driving the data collection within ETHP. However, there seems to be a lack of planning for the use of such data. Additionally, a patient-centeredness approach to the collection and use of equity data is not a priority. A patient-centered approach to health equity data and use is an approach in which patients are actively present at the decision-making table, resources are dedicated to building trust within the community, and providing variety of options for patients to share their data at the right time and place. The interviews revealed that an ideal future integrated state envisions a patient-centered and collaborative endeavor where the collection and use of data are intertwined. Implications: Moving from the current to the future state requires understanding each organization needs for equity data collection and use, addressing barriers such as size, staff shortage, and resources, and ensuring efforts are centered around the patient. Emphasizing on shared-values and vision the collaboration will be a collective effort to create a sustainable impact on reducing inequalities. Creating pooled resources, standardizing data collection while maintaining flexibility, and establishing a shared vision for data use are crucial steps. The study underscores the critical need for policies and investments with system-wide governance and leadership to encourage healthcare organizations to more effectively collect and utilize health equity data. Policies that mandate collection of health equity data should also require organizations to develop clear and comprehensive plans for utilizing these data for healthcare improvement. Additionally, promoting collaboration between healthcare settings may enhance the overall effectiveness of health equity initiatives to improve public health. More needs to be done to effectively use the data being collected to address the needs of the clients, improve population health, and decrease health inequities.
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 | 0,277 | 0,276 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,017 | 0,023 |
| Études des sciences et des technologies | 0,007 | 0,009 |
| Communication savante | 0,016 | 0,015 |
| Science ouverte | 0,007 | 0,027 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».