Impact of the COVID-19 Pandemic on the Experiences of Hospitalized Patients: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify the factors that exerted an impact on the experiences of hospitalized patients during the COVID-19 pandemic from the quality and safety perspectives. METHOD: A scoping review that followed the 5 stages described by Arksey and O'Malley was used. A systematized search of original studies was conducted in 9 databases: PubMed/MEDLINE, BDENF, CINAHL, LILACS, SciELO, Embase, Scopus, Web of Science, and Google Scholar. The factors that exerted an impact on patients' experiences were summarized, considering the perspective of quality and patient safety in health institutions. The factors were categorized using the Content Analysis technique. RESULTS: A total of 6950 studies were screened, and 32 met the eligibility criteria. The main factors that exerted an impact on the patients' experience were as follows: caregiver/family concern with the patients' well-being during hospitalization, search for alternative communication and interaction means between the patients and their family, and changes in health care organization. The restrictions inherent to the policy regarding visits and companions exerted a negative impact on the experiences, increasing the patients' feelings of loneliness and isolation. Negative impacts were also evidenced in the hospital admission and discharge process and in the limitation of treatment possibilities offered to the patients, because of contact restrictions. CONCLUSIONS: The factors that exerted an impact on the patients' experiences permeate communication between professionals, patients, and family members, with implications for health care quality.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".