A systematic review identifying effective teaching methods and their combinations for increasing empathy in physicians: pairwise and network meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Demonstrating empathy is fundamental for providing patient-centered care, however, what components are most effective is not known. Thus, the aim of this study was to identify key teaching methods and intervention characteristics for increasing empathy skills across medical training. METHOD: This study was part of a larger systematic review, which was pre-registered: CRD42018100100. We performed a systematic review, pairwise meta-analysis (PMA) and network meta-analysis (NMA). A systematic search was used across PsycINFO, Medline, CINAHL, Social Work Abstracts, ERIC, ABI/INFORM, and the Cochrane Central Register of Controlled Trials from the inception of the respective databases to October 9, 2022. Studies included randomized controlled trials (RCTs) examining behavioural interventions which targeted empathy skills for physicians and medical students. Studies were excluded if reported summary data could not be converted to an effect size; if the author were unable to be contacted; and if the study did not compare substantively different intervention combinations. Risk of bias was assessed using the Cochrane risk-of-bias tool. Data were pooled using random-effects PMA and NMA. RESULTS: 308 full-text studies were found, of which 111 met the inclusion criteria, totalling 11,111 participants. Overall, a medium effect of interventions was found [d = 0.50 (95% CI = 0.40, 0.60)], meaning the empathy skills of participants improved moderately compared to those in control groups. Publication bias was evident and heterogeneity was high (I2 = 79.19, p < .001). Subgroup analyses of the PMA revealed the following moderators were statistically significant: teaching method, intervention formats, control group; measurement type, and number of teaching methods used. The consistency assumption was met [χ2 [ (33)= 39.11, p = .21] for the NMA. The NMA revealed that didactic and rehearsal were most frequently included among the most effective teaching method combinations. CONCLUSIONS: By using PMA and NMA, we provide novel insights on effective intervention components for improving empathy in medicine. To improve aggregation of evidence, transparent and standardized reporting from studies may help reduce heterogeneity. Overall, our results support the notion that interventions need not be expensive nor prolonged to be effective.
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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 | 0,009 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».