Asymmetry in Cost Behavior in Brazilian Hospitals
Bibliographic record
Abstract
Objectives: Investigating if the proportion of fixed assets over total assets is positively associated with the asymmetric cost behavior of public and private hospitals in Brazil. Methods: In order to test the sticky cost phenomenon in a different sector of companies and industries, we used panel data regression to investigate the asymmetric cost behavior in Brazilian hospitals, analyzing the hospital cost behavior regarding the variation in revenues and verifying whether the proportion of fixed assets over total assets is positively associated with the asymmetric cost behavior. As a result, this research took the findings obtained by the models applied to data from the 101 hospitals comprising the sample, spread over the 2010–2019 period. The research was divided into four sections. The first section tested asymmetry for fixed assets over total assets for hospitals in general. The second section divided the sample into public and private hospitals. The third section analyzed the sample of conglomerates against a single hospital. Finally, the fourth section tested the asymmetry of the hospitals in the sample measured by the number of beds. Results: The evidence documented here partially confirms the results of literature on the existence of asymmetric cost behavior regarding variations in revenue. The H1 hypothesis that the proportion of fixed assets over total assets is positively associated with the asymmetric cost behavior was confirmed, especially for private and small hospitals regarding fixed assets.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".