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Enregistrement W4200090760 · doi:10.11124/jbies-21-00099

The Functional Resonance Analysis Method as a health care research methodology: a scoping review

2021· review· en· W4200090760 sur OpenAlexaff
Alexis McGill, Doug Smith, Rose McCloskey, Patricia Morris, Alex Goudreau, Brian Veitch

Notice bibliographique

RevueJBI Evidence Synthesis · 2021
Typereview
Langueen
DomainePsychology
ThématiqueHealth and Well-being Studies
Établissements canadiensUniversity of New BrunswickHorizon Health NetworkMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésHealth carePsychologyManagement scienceMedicineEngineeringPolitical science

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Objective: The objective of this review was to examine and map the literature on the use of the Functional Resonance Analysis Method (FRAM) in health care research. Introduction: The FRAM is a resilient health care tool tat offers an approach to deconstruct complex systems by mapping health care processes to identify essential activities, how they are interrelated, and the variability that emerges, which can strengthen or compromise outcomes. Insight into how the FRAM has been operationalized in health care can help researchers and policy-makers understand how this method can be used to strengthen health care systems. Inclusion criteria: This scoping review included research and narrative reports on the application of the FRAM in any health care setting. The focus was to identify the key concepts and definitions used to describe the FRAM; the research questions, aims, and objectives used to study the FRAM; the methods used to operationalize the FRAM; the health care processes examined; and the key findings. Methods: A three-step search strategy was used to find published and unpublished research and narrative reports conducted in any country. Only papers published in English were considered. No limits were placed on the year of publication. CINAHL, MEDLINE, Embase, PsycINFO, Inspec Engineering Village, ProQuest Nursing & Allied Health were searched originally in June 2020 and again in March 2021. A search of the gray literature was also completed in March 2021. Data were extracted from papers by two independent reviewers using a data extraction tool developed by the reviewers. Search results are summarized in a flow diagram, and the extracted data are presented in tabular format. Results: Thirty-one papers were included in the final review, and most (n = 25; 80.6%) provided a description or definition of the FRAM. Only two (n = 2; 6.5%) identified a specific research question. The remaining papers each identified an overall aim or objective in applying the FRAM, the most common being to understand a health care process (n = 20; 64.5%). Eleven different methods of data collection were identified, with interviews being the most common (n = 21; 67.7%). Ten different health care processes were explored, with safety and risk identification (n = 8; 25.8%) being the most examined process. Key findings identified the FRAM as a mapping tool that can identify essential activities or functions of a process (n = 20; 64.5%), how functions are interdependent or coupled (n = 18; 58.1%), the variability that can emerge within a process (n = 20; 64.5%), discrepancies between work as done and work as imagined (n = 20; 64.5%), the resiliency that exists within a process (n = 12; 38.7%), and the points of risk within a process (n = 10, 32.3%). Most papers (n = 27; 87.1%) developed models representing the complexity of a process. Conclusions: The FRAM aims to use a systems approach to examine complex processes and, as evidenced by this review, is suited for use within the health care domain. Interest in the FRAM is growing, with most of the included literature being published since 2017 (n = 24; 77.4%). The FRAM has the potential to provide comprehensive insight into how health care work is done and how that work can become more efficient, safer, and better supported.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,151
score de la tête « metaresearch » (Gemma)0,292
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,849
Score d'incertitude au seuil0,799

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1510,292
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0090,007
Bibliométrie0,0660,048
Études des sciences et des technologies0,0050,007
Communication savante0,0140,015
Science ouverte0,0050,007
Intégrité de la recherche0,0070,004
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

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.

Tête enseignante Opus0,383
Tête enseignante GPT0,616
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeRevue systématique
DomaineMéthodes
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations33
Publié2021
Routes d'admission1
Résumé présentoui

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