Mining Patterns Associated With Mobility Outcomes in Home Healthcare
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle