A Systematic Review and Qualitative Analysis of Geriatric Models of Care for Rural and Remote Populations
Notice bibliographique
Résumé
Background Access to geriatric care remains limited in rural and remote communities. To inform the development of an evidence-informed geriatric outreach model of care for rural and remote populations, we aimed to identify key operational components described in previously published geriatric models of care serving these populations. Methods Design This protocol will conform to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and be registered with Open Science Framework. Eligibility Criteria Our systematic review will include English language empirical research articles published from 1994 onwards. This time frame was selected to ensure the relevancy of the findings in the current healthcare context as well as feasibility. Articles will be included if they describe a geriatric medical model of care that had been implemented within any described rural or remote community. Articles that described conceptual models not implemented in true populations were excluded. Information sources and literature search Literature search strategies will be developed using medical subject headings (MeSH) and text words related to models of geriatric care in rural and remote settings. Studies will be identified by searching MEDLINE (OVID interface, 1994 onwards), CINAHL (EBSCO interface, 1994 onwards) and EMBASE (OVID, 1994 to present). In addition to the electronic databases, grey literature (i.e., unpublished and difficult to locate material) will be searched. Unpublished material will be identified by searching the Dissertations and Theses database as well as searching for relevant abstracts from conference proceedings via the Conference Papers Index (e.g., Canadian Association for Health Services and Policy Research [CAHSPR]). An experienced information specialist from Sinai Health System will conduct all of the literature searches. Study selection process Two reviewers will independently screen the titles and abstracts identified by the literature search for inclusion using the screening form (i.e., level 1 screening; KK and KMK). The full text of the potentially relevant articles will then be acquired and screened to determine final inclusion by the same two reviewers (i.e., level 2 screening). Resolution of any discrepancies will occur through discussion with a third reviewer (SS). Studies excluded during the full text screening phase will be documented along with an explanation for exclusion. EndNote referencing system will be used to manage the search results and screening process. Data items and data collection process Data from all included articles will be extracted using a excel data collection form. Data extraction will include details around study characteristics (e.g., author names, year of publication, country of study conduct, study design, sample size), details around the model of care (e.g., providers, implementation characteristics) and any outcome results. This data abstraction form will be pilot tested and standardized. Two reviewers will independently abstract all of the data (KK and KMK). Methodological quality/risk of bias appraisal We will use Downs and Black Checklist and the Quality Assessment for Qualitative Research Reports (QAQRR) tool to appraise the risk of bias of the included studies (Hong et al., 2018). Synthesis of included studies Data will be analyzed using a qualitative case study analytical approach to identify the core operational components that comprised each model of care to establish an objective method of comparing the various models. After a comprehensive list of identified components has been determined, we will then review each article against those components to determine what models of care can be identified as either adhering to or not adhering to each component.
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 | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».