Collective leadership to improve professional practice, healthcare outcomes and staff well-being
Notice bibliographique
Résumé
BACKGROUND: Collective leadership is strongly advocated by international stakeholders as a key approach for health service delivery, as a response to increasingly complex forms of organisation defined by rapid changes in health technology, professionalisation and growing specialisation. Inadequate leadership weakens health systems and can contribute to adverse events, including refusal to prioritise and implement safety recommendations consistently, and resistance to addressing staff burnout. Globally, increases in life expectancy and the number of people living with multiple long-term conditions contribute to greater complexity of healthcare systems. Such a complex environment requires the contribution and leadership of multiple professionals sharing viewpoints and knowledge. OBJECTIVES: To assess the effects of collective leadership for healthcare providers on professional practice, healthcare outcomes and staff well-being, when compared with usual centralised leadership approaches. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, five other databases and two trials registers on 5 January 2021. We also searched grey literature, checked references for additional citations and contacted study authors to identify additional studies. We did not apply any limits on language. SELECTION CRITERIA: Two groups of two authors independently reviewed, screened and selected studies for inclusion; the principal author was part of both groups to ensure consistency. We included randomised controlled trials (RCTs) that compared collective leadership interventions with usual centralised leadership or no intervention. DATA COLLECTION AND ANALYSIS: Three groups of two authors independently extracted data from the included studies and evaluated study quality; the principal author took part in all groups. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care (EPOC) Group. We used the GRADE approach to assess the certainty of the evidence. MAIN RESULTS: We identified three randomised trials for inclusion in our synthesis. All studies were conducted in acute care inpatient settings; the country settings were Canada, Iran and the USA. A total of 955 participants were included across all the studies. There was considerable variation in participants, interventions and measures for quantifying outcomes. We were only able to complete a meta-analysis for one outcome (leadership) and completed a narrative synthesis for other outcomes. We judged all studies as having an unclear risk of bias overall. Collective leadership interventions probably improve leadership (3 RCTs, 955 participants). Collective leadership may improve team performance (1 RCT, 164 participants). We are uncertain about the effect of collective leadership on clinical performance (1 RCT, 60 participants). We are uncertain about the intervention effect on healthcare outcomes, including health status (inpatient mortality) (1 RCT, 60 participants). Collective leadership may slightly improve staff well-being by reducing work-related stress (1 RCT, 164 participants). We identified no direct evidence concerning burnout and psychological symptoms. We are uncertain of the intervention effects on unintended consequences, specifically on staff absence (1 RCT, 60 participants). AUTHORS' CONCLUSIONS: Collective leadership involves multiple professionals sharing viewpoints and knowledge with the potential to influence positively the quality of care and staff well-being. Our confidence in the effects of collective leadership interventions on professional practice, healthcare outcomes and staff well-being is moderate in leadership outcomes, low in team performance and work-related stress, and very low for clinical performance, inpatient mortality and staff absence outcomes. The evidence was of moderate, low and very low certainty due to risk of bias and imprecision, meaning future evidence may change our interpretation of the results. There is a need for more high-quality studies in this area, with consistent reporting of leadership, team performance, clinical performance, health status and staff well-being outcomes.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; les deux têtes enseignantes 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 ».