Increasing research capacity with ICES Data & Analytic Services (DAS)
Notice bibliographique
Résumé
ABSTRACTBackgroundThe Institute for Clinical Evaluative Sciences (ICES) is a not-for-profit organization that conducts research to evaluate health care delivery and outcomes. Established in 1992, ICES houses a vast and secure array of linkable, coded health-related data on more than 13 million Ontarians, including health services data, health care provider data, registries and population-based health surveys. ICES has a reputation for generating strong evidence-based knowledge to inform policy and practice, however the use of the data was restricted to ICES’ purposes. In March 2014, ICES launched the Data & Analytic Services (DAS) platform with the primary objective of increasing access to its data to publicly-funded researchers, health care providers and administrators, policymakers and students.
 MethodDAS provides access to highly de-identified, risk-reduced datasets created from ICES’ data holdings; analytic support; and complete data analysis and report writing services. DAS also enables the importation of external data for linkage to ICES’ data holdings. Research objectives and methodology are led by the requestor and ICES analysts rely on their subject matter expertise to direct the deliverables.
 ResultsSince launch, over 200 requests from Canada, United States and United Kingdom have been adjudicated, of which 187 have been deemed feasible and eligible. Over the same period of time, ICES as an organization had over 700 active projects of which 348 were initiated, an increase in capacity of 26%. Though Toronto-based researchers represent the majority of the requests (62%), there have been requests from outside Ontario interested in comparing aspects of Ontario’s healthcare to their home province. Research topics have varied and include assessments of health care provision by sector, disease prevalence and treatment, and statistical methods. An unexpected outcome of increasing access has been the large interest from small physician groups and knowledge users who are not typically involved in research for academic purposes. Access to ICES’ data holdings provides an opportunity to examine a larger cohort of patients who share the same characteristics as their clinic patients or group. Furthermore, by enabling remote access to the data, DAS is able to leverage the capabilities of ICES’ data holdings and increase research capacity in a short period of time.
 ConclusionIn making one of the most comprehensively linked health administrative data repositories in the world widely available to the broader research and healthcare community, DAS engages investigators involved in front-line care, stimulates new avenues of research and fosters collaboration that was previously unachievable.
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 | 0,014 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,008 | 0,000 |
| Communication savante | 0,001 | 0,009 |
| Science ouverte | 0,010 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».