Characterizing Managerial Decision Making in Public Hospitals: A Case Study from Romania
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Our study investigates the primary characteristics of managerial decision-making processes in the public hospital units in Romania, particularly in the Northeast region. This research aims to delineate the decision-making model applied by managers in these units, considering the multitude of legislative, economic, technical, ethical, and organizational changes prompted by the pandemic. METHODS: A mixed-method research approach was utilized, combining semi-structured interviews and autoethnography, to capture experiences, attitudes, perceptions, motivations, and ethical considerations of decision-makers within the healthcare system. RESULTS: The findings revealed that managerial decisions in public hospitals were influenced by unique elements such as the vulnerability and support needs of patients, the absence of a clear hierarchy, the personalized nature of healthcare services, the complexity of care processes, and the use of advanced technology. External factors, notably political and economic influences, alongside internal ethical dilemmas, significantly impacted decision making. CONCLUSIONS: This study identifies the reliance on evidence-based decision making and a consultative managerial style as key to addressing these challenges. This research contributes theoretically by comparing decision-making models and practically by identifying a decision-making model that includes forms, techniques, and tools that could guide managers in decision making in Romanian public hospitals.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".