The taboo of cancer: the experiences of cancer disclosure by Iranian patients, their family members and physicians
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to describe the experiences of cancer disclosure by Iranian cancer patients, their family members and physicians. METHODS: Twenty cancer patients, ten family members and eight physicians participated in this study. Data were collected via semi-structured, in-depth interviews and analyzed using qualitative content analysis. RESULTS: Three categories were identified: cancer avoidance, a climate of non-disclosure and mutual concern. The findings demonstrated that cancer is a taboo subject and the word cancer, as well as other indicative terms, was rarely used in daily communication. A climate of non-disclosure predominated because patients were the last to know their diagnosis, they were unaware of their prognosis, and family members and physicians employed strategies to conceal this information. The mutual concern of patients, family members and physicians was the main reason that cancer was not discussed. CONCLUSION: Cancer is a taboo subject in Iran that is maintained and reinforced primarily because of the mutual concern of patients, family members and physicians. The first step to address this taboo and inform cancer patients of their diagnosis would be to understand and help mitigate the individual, family and social consequences of disclosure.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".