Contributing Factors to Safety: What Hospitalized Patients Can Tell Us? A Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND: Brazil has the second-highest COVID-19 mortality rate worldwide. While there are currently no guidelines for involving patients in their own safety, recognising patients' valuable feedback can be decisive for the safety and quality of healthcare. Thus, this study aimed to describe the patient feedback on factors contributing to safety in patients hospitalised with COVID-19 in Brazil and to examine associations with patient sociodemographic and clinical characteristics. METHODS: A cross-sectional study was conducted in nine Brazilian university hospitals. Data collection using the Patient Measure of Safety (PMOS) questionnaire was conducted by telephone with 447 patients who recovered from COVID-19. Descriptive and multilevel linear regression models were used to verify the sociodemographic characteristics associated with PMOS. RESULTS: Patients felt safer when they accessed healthcare resources, when health professionals communicated well, and when they had good teamwork skills. Sociodemographic and clinical factors influenced the patient's perception of safety. A lower perception of safety was observed among patients aged 18-39 years old, of mixed race, and who had more than six symptoms during hospitalisation. Higher perceptions of safety were identified among patients with higher education, who lived in the countryside, and who required admission to the ICU. CONCLUSIONS: This study highlighted the potential for patients to become crucial allies in ensuring safety within hospital settings by providing insights into their care, and how sociodemographic characteristics can influence the perception of safety.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".