The process of nurses’ confrontation with ethical conflicts in home care: a grounded theory study
Bibliographic record
Abstract
BACKGROUND: Ethical conflicts (ECs) are an inseparable part of nursing care, particularly home care (HC). Nurses’ inability to effectively manage ECs may lead to occupational stress, job burnout, and low patient care quality. Despite various studies on ECs, there is limited research regarding the process of nurses’ confrontation with them in HC settings. AIM: This study aimed to explore the process of nurses’ confrontation with ECs in HC. RESEARCH DESIGN: This qualitative study was conducted from February 2023 to August 2025 using Corbin and Strauss’s approach to grounded theory. METHODS: Twenty-two unstructured and semi-structured interviews were held with sixteen nurses recruited through purposeful and theoretical sampling from HC centers in Tehran, Iran. Data were analyzed using Corbin and Strauss’s approach to grounded theory. RESULTS: The main concern of participants was fear of harming patients, and their four strategies to manage it were expedient thinking, persuading, self-justifying, and disclosing. They used these strategies to balance the interests of all parties involved in the EC situation, including patients, families, colleagues, and HC center authorities. Therefore, the core category of the theory formulated in this study was “Balancing the interests”. CONCLUSION: Nurses’ confrontation with ECs in HC is a complex process influenced by a variety of contextual factors. Study findings can be used to develop purposeful educational and supportive programs for improving nurses’ ethical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".