Quality, methods, and recommendations of systematic reviews on measures of evidence-based practice: an umbrella review
Notice bibliographique
Résumé
OBJECTIVES: The objective of the review was to estimate the quality of systematic reviews on evidence-based practice measures across health care professions and identify differences between systematic reviews regarding approaches used to assess the adequacy of evidence-based practice measures and recommended measures. INTRODUCTION: Systematic reviews on the psychometric properties of evidence-based practice measures guide researchers, clinical managers, and educators in selecting an appropriate measure for use. The lack of psychometric standards specific to evidence-based practice measures, in addition to recent findings suggesting the low methodological quality of psychometric systematic reviews, calls into question the quality and methods of systematic reviews examining evidence-based practice measures. INCLUSION CRITERIA: We included systematic reviews that identified measures that assessed evidence-based practice as a whole or of constituent parts (eg, knowledge, attitudes, skills, behaviors), and described the psychometric evidence for any health care professional group irrespective of assessment context (education or clinical practice). METHODS: We searched five databases (MEDLINE, Embase, CINAHL, PsycINFO, and ERIC) on January 18, 2021. Two independent reviewers conducted screening, data extraction, and quality appraisal following the JBI approach. A narrative synthesis was performed. RESULTS: Ten systematic reviews, published between 2006 and 2020, were included and focused on the following groups: all health care professionals (n = 3), nurses (n = 2), occupational therapists (n = 2), physical therapists (n = 1), medical students (n = 1), and family medicine residents (n = 1). The overall quality of the systematic reviews was low: none of the reviews assessed the quality of primary studies or adhered to methodological guidelines, and only one registered a protocol. Reporting of psychometric evidence and measurement characteristics differed. While all the systematic reviews discussed internal consistency, feasibility was only addressed by three. Many approaches were used to assess the adequacy of measures, and five systematic reviews referenced tools. Criteria for the adequacy of individual properties and measures varied, but mainly followed standards for patient-reported outcome measures or the Standards of Educational and Psychological Testing. There were 204 unique measures identified across 10 reviews. One review explicitly recommended measures for occupational therapists, three reviews identified adequate measures for all health care professionals, and one review identified measures for medical students. The 27 measures deemed adequate by these five systematic reviews are described. CONCLUSIONS: Our results suggest a need to improve the overall methodological quality and reporting of systematic reviews on evidence-based practice measures to increase the trustworthiness of recommendations and allow comprehensive interpretation by end users. Risk of bias is common to all the included systematic reviews, as the quality of primary studies was not assessed. The diversity of tools and approaches used to evaluate the adequacy of evidence-based practice measures reflects tensions regarding the conceptualization of validity, suggesting a need to reflect on the most appropriate application of validity theory to evidence-based practice measures. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020160874.
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 | 0,467 | 0,716 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,008 |
| Méta-épidémiologie (sens large) | 0,028 | 0,021 |
| Bibliométrie | 0,070 | 0,045 |
| Études des sciences et des technologies | 0,006 | 0,010 |
| Communication savante | 0,020 | 0,018 |
| Science ouverte | 0,012 | 0,015 |
| Intégrité de la recherche | 0,014 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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; 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 ».