A concept analysis of white lie from nurses’ perspectives: A hybrid model
Bibliographic record
Abstract
Background & Aim: White lie is one of the inevitable challenges that creates an ethical dilemma during the patient care process. White lie remains an abstract concept in caring process. The aim of this study was to analyze the concept of white lie in the caring process using a hybrid model. Methods & Materials: A hybrid model of concept analysis including three phases was used in this study. In the theoretical phase, different databases including PubMed, CINAHL, Scopus, Science Direct, Google scholar, SID and Magiran were searched for finding relevant articles published in 1980-2018. The keywords were truth, white lie, care and deception (in Persian and English). In the fieldwork phase, semi-structured in depth interviews were conducted with nurses. In next step, by combining the two previous stages, the final analysis was performed. Results: In the theoretical phase, the attributes of the concept were determined, including “harmlessness”, “without personal motivation” and “use in compulsion situations”. In the fieldwork phase, three main categories such as “the sweetness of the bitter truth”, “harmless sentences to prevent harm” and “temporary relief to balance the situation” were identified from the data analysis. By merging the concepts extracted from the theoretical and fieldwork phases, “white lie in the patient care process” was defined as “an ethical decision without personal motivation, which is chosen in unstable situations to prevent predictable harms to the patient in facing the bitter truth”. Conclusion: Although a definition of white lie was developed based on the above three phases, the further development of this concept requires a deeper look at the Iranian-Islamic culture. Therefore, further research is recommended in other medical centers in the country.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".