Managers’ Competences in Private Hospitals for Investment Decisions during the COVID-19 Pandemic
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
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.
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.
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 | 0.001 | 0.002 |
| 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