EDUCATING HOME CARE NURSES ABOUT DEPRESCRIBING APPROACHES TO PROMOTE ACTIVE LIVING OF FRAIL OLDER ADULTS AT HOME
Bibliographic record
Abstract
Deprescribing is considered to be an essential part of the prescribing process where healthcare providers carefully assess the need for backing off when medication dosages are too high, or stopping medications that are no longer needed on an ongoing basis. One important role of nurses in homecare is medication management, and therefore educational training must be developed to support homecare nurses in the development of their awareness and understanding of de-prescribing approaches to help enable the opportunities for active and independent living of the frail elders at home. This study used qualitative exploratory research design, consisting of two focus group interviews with eleven homecare nurses in Ontario, Canada. Content analysis method was used for data analysis process. The findings of the study illustrate that there is a need for undertaking deprescribing approach from homecare nurse’s perspective. Nurses indicated that lack of communication, education, and collaboration among inter-professional healthcare providers act as major barrier to practice safe deprescribing approaches. Furthermore, participants have acknowledged that there is a need for basic education of deprescribing for healthcare providers, informal caregivers, and older adults. The outcome of this project includes the development of innovative training tools that help support homecare nurses in identifying at risk older adults who are vulnerable in maintaining their independence in ADL and IADL due to polypharmacy. Our research will help lead to the future development of programs about safer medication management which will foster a supportive and collaborative relationship between the homecare team, frail elders and their informal caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".