Effects of an Intervention Approach Based on the Meanings of Vocal Behaviours in Older People Living with a Major Neurocognitive Disorder: A Pilot Study
Bibliographic record
Abstract
Introduction: Vocal behaviours (VB) are frequent in long-term care facilities (LTCF) and have many negative consequences. Interventions to decrease VB have limited clinical impact. Objective: This pilot study aimed to provide preliminary evidence on the effects of an intervention approach based on the meanings of VB in older people living with a neurocognitive disorder in LTCF. Methods: A mixed methods action research study was used. Fourteen triads (n=43) made up of an older person who manifested VB, a family member, and formal caregivers were included. An approach based on the meanings of VB was implemented in five LTCF. Semi-structured interviews took place with some participants. Tools were used at four-time points to measure five variables: the frequency of VB, the well-being of older people, the perceived disruptiveness of VB, the partnership-based decision-making and the empowerment felt by family and formal caregivers. Results: The approach improved the perception of family and formal caregivers toward older people. This led to habit changes that influenced positively the frequency of VB and the well-being of older people. Formal and family caregivers also perceived VB as less disturbing and felt more empowered relatively to VB. The attitude of formal caregivers toward families evolved positively. No other changes were noted on partnership-based decision-making. Discussion and conclusion: This study indicates the potential of this approach to improve the well-being of older people who manifested VB, their families, and formal caregivers. This approach could be implemented in LTCF, tested and adapted for other behaviours.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".