Interprofessional collaboration between health professional learners when breaking bad news: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to examine pedagogies used to teach interprofessional health learners how to break bad news collaboratively. INTRODUCTION: Breaking bad news is a skill health care professionals must be equipped to deliver well, yet literature shows that this skill receives little attention in program curricula. Consequently, health care professionals feel inadequately prepared to deliver bad news, leading to greater burnout, distress, and fatigue. INCLUSION CRITERIA: Studies that describe pedagogies used to teach breaking bad news will be considered for inclusion. Studies must include two or more undergraduate and/or postgraduate learners working towards a professional health or social care qualification or degree at a university or college. Studies including lay, complementary and alternative, or non-health or social care professional learners will be excluded. METHODS: The JBI three-step process will be followed for developing the search. Databases to be searched include MEDLINE, CINAHL, Embase, Education Resource Centre, and Social Work Abstracts. Title and abstract screening through to data extraction will be completed by two independent reviewers and any disagreements will be resolved through discussion, or with a third reviewer. Results will be presented in tabular or diagrammatic form, together with a narrative summary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".