Quality of care transitions from hospital to home for COVID-19 patients discharged from Brazilian university hospitals
Bibliographic record
Abstract
Purpose To analyze the quality of transitional care for patients with COVID-19 at discharge from Brazilian university hospitals. Design/methodology/approach A cross-sectional descriptive study was carried out in five Brazilian university hospitals between April and December 2021. The sample consisted of 527 participants. Data collection consisted of a sociodemographic questionnaire and the Care Transitions Measure (CTM-15), a care transition assessment instrument, which was translated and validated in Portuguese. Findings Most participants were patients ( n = 369; 70.0%), with primary school completion ( n = 218; 43.4%), multiracial ( n = 218; 43.5%) and with an income of up to two minimum wages ( n = 182; 42.8%). Dimension 1 – management preparation – obtained the highest score (71.2 points, SD = 16.5), while Dimension 4 – care plan – obtained the lowest score (62.2 points, SD = 23.4). Among the participating hospitals, there was a difference in the overall mean with results ranging from 67.0 to 72.9 points. Originality/value A satisfactory quality of care transition was found, considering the context of a pandemic. The main weaknesses in the care transitions were related to the care planning after hospital discharge.
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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 | 0.001 | 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 | 0.000 | 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".