Coaching to develop leadership of healthcare managers: a mixed-methods systematic review
Notice bibliographique
Résumé
INTRODUCTION: Coaching is commonly used to facilitate leadership development among healthcare managers. However, there is limited knowledge of the components of coaching interventions and their impacts on healthcare managers' leadership development. This mixed-methods systematic review aimed to synthesize evidence of coaching to develop leadership among healthcare managers. METHODS: The authors conducted a mixed-methods systematic review using a convergent synthesis design where quantitative and qualitative evidence was collected and analyzed concurrently using a matrix synthesis method. They reviewed studies published in English or Chinese by searching databases including MEDLINE (Ovid), CINAHL, Embase, Cochrane Library, Nursing & Allied Health Premium, Scopus, Wanfang, CNKI, SinoMed, and VIP databases from their inception to August 10, 2023, and updated the search again on July 9, 2024. Articles were screened and assessed for eligibility. First, from eligible studies, the qualitative data were extracted to describe intervention components, the perceived impact of coaching, and participants' perceptions of being involved in coaching intervention. Second, quantitative data analysis was conducted to describe the impact of coaching interventions and the frequency of each theme evolved in the data. Third, qualitative and quantitative data were synthesized using the matrix synthesis method. RESULTS: A total of 13 studies were included in the analysis. Three qualitative studies were assessed as having 'no or few limitations', three case series studies were scored between five and eight out of 10 points, two quasi-experimental studies showed 'moderate' overall bias, and the five mixed-methods studies scored from 40 to 60% (out of 100%). For Objective 1, which covers the component of coaching (aims, ingredients, mechanism, and delivery), the typical aim of coaching interventions was to develop the leadership skills of middle management managers. The ingredients of coaching encompassed three distinct coaching categories and seven specific procedures. The mechanisms of most coaching interventions were based on theory and empirical evidence. The average delivery time was approximately four months. Overall, coaching positively impacts outcomes for managers, organizations, and staff (Objective 2). Perceptions of the participants toward coaching interventions were divided into six categories: barriers, facilitators, effective components, attitudes, satisfactory aspects, and suggestions for designing high-quality coaching interventions to improve leadership (Objective 3). CONCLUSIONS: The components of coaching interventions varied across different studies. The impact of coaching on leadership development was positive across three levels (manager, organization, and staff). Therefore, we recommend coaching as an intervention for healthcare managers aiming to enhance their leadership level. Future coaching interventions may achieve greater effectiveness if they are specifically aligned with the participants' perceptions identified in our study.
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 | 0,061 | 0,144 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,013 | 0,011 |
| Bibliométrie | 0,020 | 0,016 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».