Self-reported measurement systems to guide decision-making : a scoping review
Notice bibliographique
Résumé
There is growing interest on founding healthcare system based on value. Value is here the outcomes acheived by patient per the cost. For a long time, high income countries took in account only the clinical outcomes measures (clinical indicator) as the important outcomes to consider. But, theses measures were limitated because they weren't captured how patients felt the cares they received especially their satisfaction of theses cares. Theses aspects are very important to assess the quality of the care patients received from the healthcare system. They are also important component of health care system performance. Thus recently, high income countries moved their interests on the use of patient reported outcomes measures (PROMS) and patient reported experiences measures (PREMS). Although , many country or theirs health regions or health facilities implementated a system to collect theses measures for clinical decision making or research ; there is no evidence to date ; that help or enhance policy decision making or decision making on goverment and health administator level. This systematic scoping review aimed to summary the implementation of self-reported measurement systems used to guide decision making at goverment level or region health system level. Methods Eligibility criteria : Intervention(s)/Exposure(s) : Measurement system aimed at patient or population health containing self-reported data Comparator(s)/Control(s): None Participants/Population : Government agencies, organizations, and administrations Outcomes : Impacts on decision making support ; Barriers and facilitators to implementation, Care quality improvement, patient-outcomes improvement Types of study to be included initially: Any type of empirical study or conference abstract Datasources : MEDLINE, Embase, CINAHL, PsychINFO, Web of Science and Academic Search Premier Data extraction : Data extraction will be conducted with a standardized, pilot-tested form by one reviewer, and another will verify. Discrepancies will be resolved by discussion or by a third reviewer (senior). Extracted data will include: characteristics of the study (ex: year of publication, review type, inclusion criteria), population (ex: level of governance, country, type of institution), interventions (ex: type of system, data collection, measurement included), and outcomes (impact, best practices, gaps). Risk of bias assessement : MMAT Data synthesis :We will present descriptive statistics (ex: means, range) to describe characteristics of included reviews. For qualitative data, we will use a content analysis approach by grouping data into themes. Data will be summarized in a narrative way. Data synthesis will focus on providing information to our knowledge users regarding the impact, best practices, gaps, and challenges. We will contextualize this information for the Canadian context. Analysis of subgroups or subsets: None Results : - Description of Self-reported measurement systems framework : process , inputs and outputs - Description of Self-reported data use to decision making : differents of use like conceptual use, persuasive use, public action -Description of barriers and facilitators
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 | 0,131 | 0,319 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,010 |
| Bibliométrie | 0,027 | 0,028 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; 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 ».