The safety challenges of therapeutic self-care and informal caregiving in home care: A qualitative descriptive study
Bibliographic record
Abstract
With an increasing number of older people who require homecare services, clients must develop a therapeutic self-care ability in order to manage their health 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. This qualitative research study utilized one-on-one, in-depth, semi-structured interviews with the clients and their informal caregivers recruited from one homecare agency in Ontario, Canada. The goal of the interviews was to gain a better understanding of the relationship between client's therapeutic self-care ability and homecare safety outcomes, and the role of self-care and caregiving activities in supporting homecare safety in relation to chronic disease management. A total of fifteen older homecare clients (over the age of 65) and fifteen informal caregivers were interviewed in their homes. Qualitative description was the methodological approach used to guide the research study. Thematic analyses of the qualitative interview data revealed that homecare clients and their informal caregivers are struggling with multiple aspects of safety challenges. The study findings provided insight into safety problems related to therapeutic self-care at home, and this knowledge is vital to policy formulation related to the role of healthcare professionals in improving client's therapeutic self-care ability to reduce safety related risks and burden for older homecare recipients. Protocol Reference and REB approval (#27223) was obtained from University of Toronto Research Ethics Board.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".