Ethical Decision-Making in Public Hospitals Management: Challenges and Models from Romania
Bibliographic record
Abstract
Ethical decision-making is essential in healthcare management, particularly in addressing challenges such as resource constraints, stakeholder conflicts, and legislative ambiguities. The aim of this study is to explore the ethical decision-making process in public hospital management, including its challenges and models. The research objectives are to identify the ethical decision-making models employed by public hospital managers in Romania, to investigate how the ethical dilemmas influence the decision-making process in Romanian public hospital management and to determine the role of ethical values in the decision-making process undertaken by Romanian public hospital managers. To this end, quantitative survey data were collected from hospital managers to assess how ethical considerations shape managerial choices. The main research results reveal that ethical dilemmas, especially in areas like resource allocation and strategic planning, delay decision-making and increase its complexity. Ethical values such as fairness, transparency, and trust are central to guiding decisions, yet the lack of formal ethics training among many managers limits their ability to address these dilemmas effectively. Structured frameworks like the PLUS and IDEA models, while valuable, are underutilized, further hindering consistent ethical decision-making. This study highlights the need for mandatory ethics training, institutionalized decision-making models, and strengthened organizational policies to improve.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".