Interprofessional collaboration between health professional learners when breaking bad news: a scoping review of teaching approaches
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".