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Enregistrement W2128645079 · doi:10.3109/13561820.2011.577626

Producing and translating health system evidence for improved global health

2012· letter· en· W2128645079 sur OpenAlexaff
Steven J. Hoffman, Julio Frenk

Notice bibliographique

RevueJournal of Interprofessional Care · 2012
Typeletter
Langueen
DomaineHealth Professions
ThématiqueInterprofessional Education and Collaboration
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésHealthcare systemPsychologyHealth careMedicinePolitical science

Résumé

récupéré en direct d'OpenAlex

Dean of the Faculty, Harvard School of Public Health and T & G Angelopoulos Professor ofPublic Health and International Development, Harvard Kennedy School and Harvard School of Public Health, Boston, MA, USAEvidence is mounting to suggest that interprofessionalcollaboration is an innovative strategy that governmentsworldwide can use to strengthen their national healthsystems and improve population health outcomes (WHO,2010). From a global health perspective, interprofessionalcollaboration offers a way to synergistically maximize thecontributions of every available health worker whileconserving limited resources. Interprofessional collaborationis also said to result in more flexible health workforces thatare better prepared to tackle unexpected challenges. Evidencefrom empirical studies and systematic reviews highlightseveral system-wide benefits of interprofessional collabor-ation which are relevant to achieving global health goals.These include improved quality of services, access to care andpatient safety as well as reductions in costs, hospitaladmissions and mortalities (CHSRF, 2006; Reeves, Goldman,Burton, & Sawatzky-Girling, 2010; Reeves, et al., 2008;Zwarenstein, Goldman, & Reeves, 2009). Interprofessionalcollaboration is also increasingly recognized as an importantpart of broader efforts to strengthen national health systems.Calls for transforming the way health professionals areeducated and the manner in which they practice areaccordingly growing louder (Frenk et al., 2010).However, equally important to education and practicereforms are the health system policies that must be enacted toenable, support and sustain interprofessional collaboration.Various mechanisms have recently been demonstrated toinfluence the success or failure of interprofessionalcollaboration in national health systems. The World HealthOrganization, for example, recently emphasized howsupportive funding streams, remuneration models, capitalplanning, regulation, professional registration, accreditationand risk management can contribute to effective inter-professional collaboration (WHO, 2010). The importanceof appropriate clinical governance models, nationalhealth legislation, integrated information systems andcommunication platforms has also recently been highlighted(Mickan, Hoffman, & Nasmith, 2010).At this point in time, very little research or otherknowledge is available on whether, how and why particularhealth system reforms aiming to promote interprofess-ional collaboration actually achieve positive outcomes.Considerable research has been amassed that focuses onindividual- and institution-level interventions and out-comes, but few inquiries have been conducted at the systemlevel of analysis. In particular, policymakers often ask forevidence on the comparative effectiveness, cost and likelystakeholder responses to implementing the various healthsystem interventions presented to them. Increasing theproduction of high-quality, policy-relevant and locallyapplicable health system evidence on interprofessionalcollaboration is therefore a strategic opportunity for theinterprofessional community to make a significant contri-bution to national and global health efforts. More of thisknowledge will help shape whether and how governmentsworldwide invest in this strategy.In terms of actually producing this knowledge, it is truethat health systems are complex and challenging to study, butthey are certainly not “black boxes” that are too complicatedor intricate to understand. Everyday new knowledge isuncovered on what works and what does not work, and why,in different health system contexts (Frenk, 2010). What isunfortunate is that too often opportunities to evaluate healthsystem reforms are not seized. This appears to be the casefor interprofessional collaboration: the past decade haswitnessed various major system-wide initiatives on nearlyevery continent, yet very few of them have been rigorouslyevaluated. Indeed, while efforts to support interprofessionalcollaboration have been reported in at least 41 countriesworldwide (Rodger & Hoffman, 2010), knowledge of the

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,489
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,073
Tête enseignante GPT0,502
Écart entre enseignants0,429 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations2
Publié2012
Routes d'admission1
Résumé présentoui

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