Association between age and patients’ perceptions of safety in hospitals during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Aim This study aimed to explore patients' perceptions of factors contributing to patient safety during hospitalization for COVID-19, with a focus on differences among three distinct adult age groups. Methods A cross-sectional study was conducted. Data were collected from 533 patients hospitalized with COVID-19 in nine Brazilian university hospitals between April and December 2021. Sociodemographics, health conditions, and the Patient Measure of Safety (PMOS) data were gathered via telephone interviews. Descriptive statistics and ANOVA were used to compare the mean scores across PMOS items, domains, and total scores by age groups (18–44; 45–64; and 65+ years). Results Patients aged 45–64 years reported the highest perception of safety, while those aged between 18 and 44 years reported the lowest. Statistically significant differences in safety perceptions were observed across age groups. The younger adult group (18–44 years) reported lower overall PMOS scores, particularly in areas such as equipment, access to resources, and organization and care planning, compared to the middle-aged group. Younger adults were more critical of aspects including attentiveness to patient concerns, availability of health professionals, space, and equipment, teamwork skills, and information sharing between the healthcare team and patients. Conclusion Perception of factors contributing to patient safety during the COVID-19 pandemic varied across different age groups. Understanding these differences can inform the development of targeted strategies to incorporate patient perspectives into hospital safety initiatives, indicators development, and research to improve patient safety in hospital settings.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".