Truth-telling and hematopoietic stem cell transplantation
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplantation is a potential cure for a range of life-threatening diseases, but is also associated with a high mortality rate. Nurses encounter a variety of situations wherein they are faced with discussing bad news with hematopoietic stem cell transplantation patients. RESEARCH OBJECTIVE: The aim of this study was to explore the experiences and strategies used by Iranian nurses related to truth-telling and communicating bad news to hematopoietic stem cell transplantation patients. RESEARCH DESIGN: A qualitative approach using content analysis of interview data was conducted. PARTICIPANTS AND RESEARCH CONTEXT: A total of 18 nurses from the main hematopoietic stem cell transplantation center in Iran participated in semi-structured interviews. ETHICAL CONSIDERATIONS: The Institutional Review Board of the Tabriz University of Medical Sciences and the Hematology-Oncology and Stem Cell Transplantation Research Center affiliated with the Tehran University of Medical Sciences approved the study. FINDINGS: In the first main category, not talking about the disease and potential negative outcomes, the nurses described the strategies of not naming the disease, talking about the truth in indirect ways and telling gradually. In the second main category, not disclosing the sad truth, the nurses described the strategies of protecting patients from upsetting information, secrecy, denying the truth and minimizing the importance of the problem. The nurses used these strategies to minimize psychological harm, avoid patient demoralization, and improve the patient's likelihood of a fast and full recovery. DISCUSSION: The priority for Iranian hematopoietic stem cell transplantation nurses is to first do no harm and to help patients maintain hope. This reflects the Iranian healthcare environment wherein communicating the truth to hematopoietic stem cell transplantation patients is commonly considered inappropriate and avoided. CONCLUSION: Iranian nurses require education and support to engage in therapeutic, culturally appropriate communication that emphasizes effective techniques for telling the truth and breaking bad news, thereby potentially improving patient outcomes and protecting patient rights.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".