The Relationship between Governance and Earnings Management: An Advanced Empirical Study of Non-profit Hospitals in Taiwan
Bibliographic record
Abstract
In response to criticism regarding the financial information of nonprofit proprietary hospitals in Taiwan, the Taiwan Department of Health (February 2006) established standards for the financial reports of medical-juridical persons. These guidelines stipulate that such reports must be audited by a certified public accountant to verify that the reported earnings are representative. However, nonprofit proprietary hospitals continue to transfer hospital profits to individuals or corporate groups by using diverse measures, indicating that earnings figures may not reflect operational performance. Therefore, this study investigated nonprofit proprietary hospitals in Taiwan and applied the logistic regression method to examine earnings management (EM) behavior. The empirical results showed that the governance index exhibited a negative correlation with discretionary accruals of bad debt, discretionary accruals of the Jones model, and discretionary accruals of non-operating or non-revenue-generating activity. Nevertheless, discretionary items play an active role in EM. Discretionary accruals of non-operating or non-revenue-generating activity possessed relatively strong explanatory power.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".