THE USE OF NON-PHARMACOLOGICAL INTERVENTIONS TO REDUCE AGITATION IN SENIORS WITH DEMENTIA
Bibliographic record
Abstract
This phenomenological qualitative study was conducted to gain insight into the use of non-pharmacological interventions (NPIs) to reduce agitation in seniors with dementia who live in long-term care homes. The perceptions and experiences were captured from 44 long-term care staff from four public and one private home in London, Ontario. Twelve individual interviews and five focus groups were held across the sites with recreation, nursing, dietary, personal support workers and upper management staff. All transcripts were analyzed using inductive content analysis. The perceived barriers to using NPIs were lack of time, low staff-to-resident ratios, the unpredictable and short-lasting effectiveness of NPIs in reducing agitation, and the physical environmental conditions of the LTC homes, such as the high volume of residents in a small space and increased noise levels. The major facilitators to the use of NPIs included consistency in staff and the resident’s routine, that all staff were able to implement NPIs whereas not all staff can distribute medications, adequate resources, and teamwork. Both medications and NPIs were used to manage agitation, although staff expressed a need to strive for more frequent use of NPIs because they were perceived to increase the quality of life of the residents.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".