Comparative analysis of patients’ satisfaction level, hospitalized before and during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction and aim. Measurement of the satisfaction level with health services is the most frequently used indicator, mainly because of its importance for determining the quality of the care offered. It is the key to succeed in achieving high-quality healthcare. The purpose of this study was to create a retrospective comparative analysis of the satisfaction level amongst patients hospitalized before and during the COVID-19 pandemic. Material and methods. The study covered a total of 966 patients in 19 hospital wards, in the fourth quarter of 2019 (before the pandemic) and in the second quarter of 2021 (during the pandemic) at the Masovian Specialist Hospital in Radom. The level of patient satisfaction was assessed based on the questionnaire prepared and approved by the Quality Assurance Team in the Masovian Specialist Hospital. The statistical analysis was carried out on the basis of the STATISTICA 10.1 program, using the Pearson’s chi square test, for the significance level at p<0.05. Results. The high level of satisfaction of patients staying in the hospital during the pandemic applied to the widely understood medical and nursing care as well as sanitary conditions in wards, especially the cleanness of rooms, bed linens and sanitary facilities. Conclusion. The biggest dissatisfaction of hospitalized patients during the COVID-19 pandemic involved certain restrictions of visitations and using pastoral services.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".