Examining the relationship between therapeutic self-care and adverse events for home care clients in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: In an era of a rapidly aging population who requires home care services, clients must possess or develop therapeutic self-care ability in order to manage their health conditions safely in their homes. Therapeutic self-care is the ability to take medications as prescribed and to recognize and manage symptoms that may be experienced, such as pain. The purpose of this research study was to investigate whether therapeutic self-care ability explained variation in the frequency and types of adverse events experienced by home care clients. METHODS: A retrospective cohort design was used, utilizing secondary databases available for Ontario home care clients from the years 2010 to 2012. The data were derived from (1) Health Outcomes for Better Information and Care; (2) Resident Assessment Instrument-Home Care; (3) National Ambulatory Care Reporting System; and (4) Discharge Abstract Database. Descriptive analysis was used to identify the types and prevalence of adverse events experienced by home care clients. Logistic regression analysis was used to examine the association between therapeutic self-care ability and the occurrence of adverse events in home care. RESULTS: The results indicated that low therapeutic self-care ability was associated with an increase in adverse events. In particular, logistic regression results indicated that low therapeutic self-care ability was associated with an increase in clients experiencing: (1) unplanned hospital visits; (2) a decline in activities of daily living; (3) falls; (4) unintended weight loss, and (5) non-compliance with medication. CONCLUSIONS: This study advances the understanding about the role of therapeutic self-care ability in supporting the safety of home care clients. High levels of therapeutic self-care ability can be a protective factor against the occurrence of adverse events among home care clients. A clear understanding of the nature of the relationship between therapeutic self-care ability and adverse events helps to pinpoint the areas of home care service delivery required to improve clients' health and functioning. Such knowledge is vital for informing health care leaders about effective strategies that promote therapeutic self-care, as well as providing evidence for policy formulation in relation to risk mitigation in home care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".