Study on Attitudes of Cancer Patients about Breaking Ways of Bad News
Bibliographic record
Abstract
Background and Aim : Giving bad news is an inevitable part of medical profession. There are different opinions about telling the truth and giving bad news to patients in different cultures and societies. Thus, purpose of this study was investigating attitudes of cancer patients about methods of awareness of bad news. Materials and Methods : The study was cross-sectional research that has been done on a sample of 160 people of cancer patients. This study used questionnaire that its validity was confirmed by communication sciences professors and medical experts. The reliability of questionnaire and Cronbach's alpha for all items of the questionnaire was 95 percent in estimating validity of questionnaire was determined that all questions were significantly correlated. SPSS software, regression analysis and Fisher test were used. Ethical Considerations : In this study, verbal informed consent of participants was obtained followed by an explanation about the purpose of the study, anonymity and confidentiality of patients' information. Findings : In this study investigated 160 patient point of view.37.5% of participants were men and 62.5 of them were women (18-77years). The results show that 57.7 % of patients agreed with historical approach, 78.8% of them agreed with direct source style and 94.4% agreed with informal style of giving information. 18.1% of patients disagree with the flow of the companions of patients of the disease. Conclusion : Findings indicates on better breaking of the bad news to the patients with a historical approach and telling to the patient by physician, directly. Citation: Karimi Rahjerdi A, Nasiri B, Shamshiri AR. Study on Attitudes of Cancer Patients about Breaking Ways of Bad News. Bioeth Health Law J. 2017; 1(2):49-54.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".