Characterizing Managerial Decision Making in Public Hospitals: A Case Study from Romania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it