The perspectives of patients, family members and healthcare professionals on readmissions: preventable or inevitable?
Bibliographic record
Abstract
An understanding of what complex medical patients with chronic conditions, family members and healthcare professionals perceive to be the key reasons for the readmission is important to preventing their occurrence. In this context, we undertook a study to understand the perceptions of patients, family members and healthcare professionals regarding the reasons for, and preventability of, readmissions. An exploratory case design with semi-structured interviews was conducted with 49 participants, including patients, family members, nurses, case managers, physicians, discharge planners from a general internal medicine unit at a large and academic hospital. Data were analyzed using a directed content analysis approach that involved three investigators. Two contrasting themes emerged from the analysis of interview data set. The first theme was readmissions as preventable occurrences. Our analyses elucidated contributing factors to readmissions during the patients' hospital stay and after the patients were discharged. The second theme was readmissions as inevitable, occurring due to the progression of disease. Our study findings indicate that some readmissions are perceived to be inevitable due to the burden of disease while others are perceived to be preventable and associated with factors both in hospital and post-discharge. Continued interprofessional efforts are required to identify patients at risk for readmission and to organize and deliver care to improve health outcomes after hospitalization.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".