A Hermeneutic Interpretation of Nurses’ Experiences of Truth Telling and Harms in Cancer Care in Qatar
Bibliographic record
Abstract
BACKGROUND: Telling the truth to cancer patients remains under debate in the Middle East, where concealment about diagnosis and prognosis occurs in some cases. Concealment results in challenges for nurses providing care. OBJECTIVE: The aim of this study was to understand nurses' lived experiences of caring for cancer patients whose cancer diagnosis or prognosis has been withheld from them. METHODS: Eight nurses from the national cancer center in Qatar were interviewed. The transcripts of the interview texts were interpreted using Gadamer's hermeneutic approach. RESULTS: The interpretations are shaped by understandings of harm. Nurses assessed harm using empathy. Nurses' empathy was permeated with fears that accompany a cancer diagnosis; the language of cancer is interpreted as a language of fear. It is ideas about harms and evoking patients' fear that generates nurses' experiences of complexity, ambiguity, and conflicting feelings regarding truth telling and concealment. The meanings nurses drew from their experiences rested on understandings about love, vulnerability, and opportunities to atone. We interpret nurses' descriptions of being enmeshed in a web of lies through which multidimensional harms are experienced. The complexities of nurses' experiences go well beyond the universal concepts of informed consent and patients' rights. CONCLUSIONS: Nurses' experiences reveal insights that likely resonate across other jurisdictions in the Arabic Gulf and other Eastern cultures, where nurses deal with these sensitive issues case by case. IMPLICATIONS FOR PRACTICE: Leaders and health professionals in cancer care in such cultures must establish more nuanced and transparent interdisciplinary approaches to respond to the complexities of truth telling in cancer 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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".