1344 UNDERSTANDING PATHWAYS INTO CARE HOMES USING DATA (THE UNPICD STUDY)
Bibliographic record
Abstract
Abstract Introduction Moving into a care home is a significant, life-changing experience which occurs to address care needs which cannot be supported elsewhere. UK health policy recommends against moving into a care home from the acute hospital. However, this occurs in practice. Better understanding pathways into care homes could improve support for individuals and families, service planning and policymaking. Our aim was to characterise individuals who move-in to a care home from hospital and those moving-in from the community, identifying factors associated with moving-in from hospital. Method A retrospective observational cohort study was conducted involving adults moving into care homes in Scotland between 1/3/13-31/3/16 using the Scottish Care Home Census (SCHC), a national individual-level social care dataset. SCHC data were linked to routine data sources including hospital admissions, community prescribing and mortality. The data were split into those moving-in from hospital and those moving-in from the community. Descriptive statistics characterising the two groups were generated and multivariate regression undertaken to identify factors associated with moving-in from hospital. Results A total of 23,892 individuals were included in the analysis, of whom 13,564 (56.8%) moved-in from hospital. A third came directly from an acute hospital, with 57.7% from rehabilitation or community hospitals and 7.1% from inpatient psychiatry. Being male, receiving nursing care, high frailty risk, increasing numbers of hospital admissions and diagnoses of any fracture or stroke in the six months before moving-into the care home were all significant predictors of moving-in from hospital. Conclusions The population moving-in to care homes from hospital are clinical distinct from those moving-in from the community. National cross-sectoral data linkage of health and social care data is feasible, but the available data are dominated by health characteristics. There is an urgent need to operationalise other meaningful variables which shape care pathways to enhance understanding and evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".