Determinants of Qualified Audit Opinion: Empirical Study of Portuguese Private Sector Hospitals
Bibliographic record
Abstract
This study aims to identify the determinants of the auditor’s qualified opinion in 71 Portuguese privately owned hospitals from 2019 to 2021. Seven research hypotheses are defined, related to the characteristics of the audited hospitals (size, performance, and debt), the qualified opinion of the previous year and the auditor’s characteristics (type, gender, and switching). The hypotheses are statistically tested using logistic regression models and data collected from the ORBIS and SABI databases, as well as the hospital’s annual reports. The results evidence that the determinants of the qualified opinion in private sector hospitals are the qualified opinion from the previous year, the hospital’s debt level, and its performance. The first two factors have a positive influence, while performance has a negative influence. In turn, the size of the hospital and the characteristics of the auditor do not seem to influence that opinion. In the private health sector, it seems that the endogenous characteristics of the audited hospital have the most influence on the auditor’s opinion, while other factors, such as the auditor’s characteristics, do not appear to influence qualified opinion. The present study provides important contributions to theory and practice, as the qualified opinion is highly significant for more informed decision making and research related to audit opinion in the healthcare sector is very scarce.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".