Emotional Labour of Caring for Hematopoietic Stem Cell Transplantation Patients: Iranian Nurses' Experiences
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the emotional labour experienced by nurses who care for hematopoietic stem cell transplantation (HSCT) patients in Iran. METHODS: Eighteen nurses participated in semi-structured interviews. The interviews were analyzed using qualitative content analysis methods. RESULTS: Three main categories described the emotional labour involved, namely, emotional intimacy, feeling overwhelmed with the sadness and suffering, and changing self. Nurses had compassion for their patients, contributing to a close nurse-patient relationship. The nurses' emotional labour resulted in their feeling overwhelmed with sadness and suffering. Five subcategories described this emotional toll: (a) witnessing suffering, (b) struggling mentally, (c) hurting emotionally, (d) feeling drained of energy, and (e) escaping grief. Dealing with death and dying on an ongoing basis promoted the nurses' changing self. CONCLUSION: Iranian nurses who care for HSCT patients experience a range of positive and negative emotions. Establishing appropriate support systems for nurses might help mediate the negative aspects of emotional labour. thereby improving nursing work life and ultimately the quality of patient care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".