Mining Patterns Associated With Mobility Outcomes in Home Healthcare
Bibliographic record
Abstract
BACKGROUND: Mobility is critical for self-management. Understanding factors associated with improvement in mobility during home healthcare can help nurses tailor interventions to improve mobility outcomes and keep patients safely at home. OBJECTIVES: The aims were to (a) identify patient and support system factors associated with mobility improvement during home care, (b) evaluate consistency of factors across groups defined by mobility status at the start of home care, and (c) identify patterns of factors associated with improvement and no improvement in mobility within each group. METHODS: Outcome and Assessment Information Set data extracted from a national convenience sample of 270,634 patient records collected from October 1, 2008 to December 31, 2009 from 581 Medicare-certified, home healthcare agencies were used. Patients were placed into groups based on mobility scores at admission. Odds ratios were used to index associations of factors with improvement at discharge. Discriminative pattern mining was used to discover patterns associated with improvement of mobility. RESULTS: Overall, mobility improved for 49.4% of patients; improvement occurred most frequently (80%) among patients who were able, at admission, to walk only with the supervision or assistance of another person at all times. Numerous factors associated with improvement in mobility outcome were similar across the groups (except for those who were chairfast but were able to wheel themselves independently); however, the number, strength, and direction of associations varied. In most groups, data mining-discovered patterns of factors associated with the mobility outcome were composed of combinations of functional and cognitive status and the type and amount of help required at home. DISCUSSION: This study provides new data mining-based information about how factors associated with improvement in mobility group together and vary by mobility at admission. These approaches have potential to provide new insights for clinicians to tailor interventions for improvement of mobility.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".