White lie during patient care: a qualitative study of nurses’ perspectives
Bibliographic record
Abstract
BACKGROUND: Keeping the patients well and fully informed about diagnosis, prognosis, and treatments is one of the patient's rights in any healthcare system. Although all healthcare providers have the same viewpoint about rendering the truth in treatment process, sometimes the truth is not told to the patients; that is why the healthcare staff tell "white lie" instead. This study aimed to explore the nurses' experience of white lies during patient care. METHODS: This qualitative study was conducted from June to December 2018. Eighteen hospital nurses were recruited with maximum variation from ten state-run educational hospitals affiliated to Tehran University of Medical Sciences. Purposeful sampling was used and data were collected by semi-structured interviews that were continued until data saturation. Data were classified and analyzed by content analysis approach. RESULTS: The data analysis in this study resulted in four main categories and 11 subcategories. The main categories included hope crisis, bad news, cultural diversity, and nurses' limited professional competences. CONCLUSION: Results of the present study showed that, white lie told by nurses during patient care may be due to a wide range of patient, nurse and/or organizational related factors. Communication was the main factor that influenced information rendering. Nurses' communication with patients should be based on mutual respect, trust and adequate cultural knowledge, and also nurses should provide precise information to patients, so that they can make accurate decisions regarding their health 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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".