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Enregistrement W4230304840 · doi:10.1002/14651858.cd000125.pub5

Local opinion leaders: effects on professional practice and healthcare outcomes

2019· review· en· W4230304840 sur OpenAlexaff
Gerd Flodgren, Mary Ann O’Brien, Elena Parmelli, Jeremy Grimshaw

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2019
Typereview
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensOttawa HospitalUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésOpinion leadershipHealth careHealth professionalsPublic relationsPsychologyBusinessNursingMedicineMedical educationPolitical science

Résumé

récupéré en direct d'OpenAlex

Clinical practice is not always evidence-based and, therefore, may not optimise patient outcomes. Local opinion leaders (OLs) are individuals perceived as credible and trustworthy, who disseminate and implement best evidence, for instance through informal one-to-one teaching or community outreach education visits. The use of OLs is a promising strategy to bridge evidence-practice gaps. This is an update of a Cochrane review published in 2011.To assess the effectiveness of local opinion leaders to improve healthcare professionals' compliance with evidence-based practice and patient outcomes.We searched CENTRAL, MEDLINE, Embase, three other databases and two trials registers on 3 July 2018, together with searching reference lists of included studies and contacting experts in the field.We considered randomised studies comparing the effects of local opinion leaders, either alone or with a single or more intervention(s) to disseminate evidence-based practice, with no intervention, a single intervention, or the same single or more intervention(s). Eligible studies were those reporting objective measures of professional performance, for example, the percentage of patients being prescribed a specific drug or health outcomes, or both. We included all studies independently of the method used to identify OLs.We used standard Cochrane procedures in this review. The main comparison was (i) between any intervention involving OLs (OLs alone, OLs with a single or more intervention(s)) versus any comparison intervention (no intervention, a single intervention, or the same single or more intervention(s)). We also made four secondary comparisons: ii) OLs alone versus no intervention, iii) OLs alone versus a single intervention, iv) OLs, with a single or more intervention(s) versus the same single or more intervention(s), and v) OLs with a single or more intervention(s) versus no intervention.We included 24 studies, involving more than 337 hospitals, 350 primary care practices, 3005 healthcare professionals, and 29,167 patients (not all studies reported this information). A majority of studies were from North America, and all were conducted in high-income countries. Eighteen of these studies (21 comparisons, 71 compliance outcomes) contributed to the median adjusted risk difference (RD) for the main comparison. The median duration of follow-up was 12 months (range 2 to 30 months). The results suggested that the OL interventions probably improve healthcare professionals' compliance with evidence-based practice (10.8% absolute improvement in compliance, interquartile range (IQR): 3.5% to 14.6%; moderate-certainty evidence).Results for the secondary comparisons also suggested that OLs probably improve compliance with evidence-based practice (moderate-certainty evidence): i) OLs alone versus no intervention: RD (IQR): 9.15% (-0.3% to 15%); ii) OLs alone versus a single intervention: RD (range): 13.8% (12% to 15.5%); iii) OLs, with a single or more intervention(s) versus the same single or more intervention(s): RD (IQR): 7.1% (-1.4% to 19%); iv) OLs with a single or more intervention(s) versus no intervention: RD (IQR):10.25% (0.6% to 15.75%).It is uncertain if OLs alone, or in combination with other intervention(s), may lead to improved patient outcomes (3 studies; 5 dichotomous outcomes) since the certainty of evidence was very low. For two of the secondary comparisons, the IQR included the possibility of a small negative effect of the OL intervention. Possible explanations for the occasional negative effects are, for example, the possibility that the OLs may have prioritised some outcomes, at the expense of others, or that an unaccounted outcome difference at baseline, may have given a faulty impression of a negative effect of the intervention at follow-up. No study reported on costs or cost-effectiveness.We were unable to determine the comparative effectiveness of different approaches to identifying OLs, as most studies used the sociometric method. Nor could we determine which methods used by OLs to educate their peers were most effective, as the methods were poorly described in most studies. In addition, we could not determine whether OL teams were more effective than single OLs.Local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence-based practice, but the effectiveness varies both within and between studies.The effect on patient outcomes is uncertain. The costs and the cost-effectiveness of the intervention(s) is unknown. These results are based on heterogeneous studies differing in types of intervention, setting, and outcomes. In most studies, the role and actions of the OL were not clearly described, and we cannot, therefore, comment on strategies to enhance their effectiveness. It is also not clear whether the methods used to identify OLs are important for their effectiveness, or whether the effect differs if education is delivered by single OLs or by multidisciplinary OL teams. Further research may help us to understand how these factors affect the effectiveness of OLs.

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)0,020
score de la tête « metaresearch » (Gemma)0,088
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,105

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,088
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0040,008
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,002
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

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.

Tête enseignante Opus0,278
Tête enseignante GPT0,549
Écart entre enseignants0,271 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
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

Citations271
Publié2019
Routes d'admission1
Résumé présentoui

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