Interprofessional collaboration between health professional learners when breaking bad news: a scoping review of teaching approaches
Notice bibliographique
Résumé
OBJECTIVE: The objective of this scoping review was to examine teaching approaches used to teach interprofessional health professional learners how to break bad news collaboratively. INTRODUCTION: When breaking bad news, health professionals must be equipped to deliver it skillfully and collaboratively; however, the literature shows that this skill receives little attention in program curricula. Consequently, health professionals can feel inadequately prepared to deliver bad news, which may lead to increased burnout, distress, and compassion fatigue. INCLUSION CRITERIA: Studies that describe teaching approaches used to teach learners how to break bad news collaboratively were considered for inclusion. Studies must have included 2 or more undergraduate and/or postgraduate learners working toward a professional health or social care qualification/degree at a university or college. Studies including lay, complementary and alternative, or non-health/social care learners were excluded. Due to the primary language of the research team, only English articles were included. METHODS: The JBI 3-step process was followed for developing the search. Databases searched included MEDLINE (Ovid), CINAHL (EBSCOhost), Embase, Education Resource Complete (EBSCOhost), and Social Work Abstracts (EBSCOhost). The initial search was conducted on February 11, 2021, and was updated on May 17, 2022. Title and abstract screening and data extraction were completed by 2 independent reviewers. Disagreements were resolved through discussion or with a third reviewer. Results are presented in tabular or diagrammatic format, together with a narrative summary. RESULTS: Thirteen studies were included in the scoping review, with a range of methodologies and designs (pre/post surveys, qualitative, feasibility, mixed methods, cross-sectional, quality improvement, and methodological triangulation). The majority of papers were from the United States (n=8; 61.5%). All but 1 study used simulation-enhanced interprofessional education as the preferred method to teach interprofessional cohorts of learners how to break bad news. The bulk of simulations were face-to-face (n=11; 84.6%). Three studies (23.1%) were reported as high fidelity, while the remainder did not disclose fidelity. All studies that used simulation to teach students how to break bad news utilized simulated participants/patients to portray patients and/or family in the simulations. The academic level of participants varied, with the majority noted as undergraduate (n=7; 53.8%); 3 studies (23.1%) indicated a mix of undergraduate and graduate participants, 2 (15.4%) were graduate only, and 1 (7.7%) was not disclosed. There was a range of health professional programs represented by participants, with medicine and nursing equally in the majority (n=10; 76.9%). CONCLUSIONS: Simulation-enhanced interprofessional education was the most reported teaching approach to teach interprofessional cohorts of students how to break bad news collaboratively. Inconsistencies were noted in the language used to describe bad news, use of breaking bad news and interprofessional competency frameworks, and integration of interprofessional education and simulation best practices. Further research should focus on other interprofessional approaches to teaching how to break bad news; how best to incorporate interprofessional competencies into interprofessional breaking bad news education; whether interprofessional education is enhancing collaborative breaking bad news; and whether what is learned about breaking bad news is being retained over the long-term and incorporated into practice. Future simulation-specific research should explore whether and how the Healthcare Simulation Standards of Best Practice are being implemented and whether simulation is resulting in student satisfaction and enhanced learning.
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 | 0,007 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 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 | 0,002 |
| 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 ».