Bibliographic record
Abstract
Breaking bad news (BBN) is a necessary component of communication in health care. When health care practitioners (HCPs) communicate illnesses of a serious nature with Indigenous people, they often do not do so in a culturally safe way, and this can perpetuate health inequities and catalyze poor health outcomes, which are often linked to Indigenous historical trauma. In this integrative review I sought to analyze and synthesize the published experiences of Indigenous adults from Canada, the United States, New Zealand, and Australia with BBN conversations. I included twelve qualitative studies ranging from 1999 to 2022. I obtained the studies through CINAHL (EBSCO), MEDLINE (OVID), manual reference list screening, and citation tracking on Google Scholar. Themes identified were (a) Indigenous identity, (b) HCP misinterpretation, (c) the meaning of words, (d) truth-telling and the prophetic power of words, (e) indirect communication, and (f) the role of family. The review findings can inform HCPs’ understanding of potential communication errors and offer recommendations to improve culturally safe BBN conversations. However, it is important to recognize that although commonalities in experience exist, further research is needed to understand and address the unique experiences of BBN in culturally diverse Indigenous tribes and nations.,
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".