Older persons with multiple chronic conditions' experiences of unplanned readmission: An integrative review
Bibliographic record
Abstract
BACKGROUND: As persons, 60 years of age and older live longer, they are more likely to develop one or more chronic conditions. Rising numbers of older persons with multiple chronic conditions (MCCs) will increase the need for home healthcare services and hospital services and unplanned readmissions will increase globally. AIM: The aim of this integrative review was to explore the experiences of older persons with MCCs' unplanned readmission from home to hospital within 30 days of discharge using an integrative review. METHOD: Whittemore and Knafl's method was followed to address the research aim. Four databases (Ovid MEDLINE, Scopus, CINAHL and Embase) were searched between 2005 and 2020, suitability for inclusion was assessed, and data were extracted and analysed using content analysis. RESULTS: Thirteen articles (10 qualitative, one quantitative, and two mixed methods) were included in this review. Three themes emerged from the data that reflected older persons with MCCs' unplanned readmission experiences. These themes included (a) feelings of security, support and relief; (b) undesirable challenges at home (struggling to manage care and balancing support needs); and (c) unpleasant feelings and emotions (feelings of fear and mistrust, feelings of disappointment and loss, feelings of anxiousness and pressure). CONCLUSION: Research about unplanned readmission to the hospital does not provide sufficient detail or understanding about older persons with MCCs' experiences or their psychosocial experiences. Addressing research gaps related to the psychosocial processes and factors associated with unplanned readmission is needed to expand the current understanding of the process and concept of unplanned readmission.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".