Managers’ Competences in Private Hospitals for Investment Decisions during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has posed an unprecedented challenge for health systems worldwide. The increased demand for investment in hospitals has become one of the greatest financial vulnerabilities, and in this context, the manager’s involvement in decision-making is associated with better analysis in order to achieve better results. This article aims to define a model to outline the manager profile in private hospitals, as well as the process and the relationship with investment decision-making, so as to guide future work to improve institutions’ performance and ensure the sustainability of patient care processes and the use of resources. Semi-structured interviews were held with an administrative (or financial) director in Brazil, Canada and Portugal and analyzed by the conventional content analysis method and coded, using NVivo 11, identifying the main topics. A model for investment decision-making is proposed to improve resource allocation and performance. The results indicate, for multidisciplinary training, where managers contribute to an efficient use of resources and contribute to the maintenance of quality of care, including about investment and financing of hospitals, where performance analysis reflects on decision-making.
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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 | 0.011 | 0.021 |
| 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 | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".