Technology implementation in care practices for community-dwelling older adults with mild cognitive decline: Perspectives of professional caregivers in Quebec and Brussels
Bibliographic record
Abstract
Objective: As worldwide population aging is accelerating, innovative technologies are being developed to support independent living among community-dwelling older adults with mild cognitive decline. However, the successful implementation of these interventions is often challenging. Until now, literature on implementation issues related to the specific context of older adults with mild cognitive decline is lacking and the few studies available do not focus specifically on the perspective of professional caregivers. Yet the perspective of these caregivers is important as they can be considered a key facilitator for technology implementation among this population. Therefore, this study was the first to examine technology implementation among community-dwelling older adults with mild cognitive decline from the broader perspective of professional caregivers. Methods: = 8). Braun and Clarke' method for thematic analysis, guided by a qualitative descriptive approach was applied to inductively identify themes from the data. Results: We identified factors influencing technology implementation in older adults with mild cognitive decline on three levels: an individual level (e.g., characteristics of older adults with mild cognitive decline and professional caregivers' attitude), an organizational level (e.g., lack of training among professional caregivers) and a level referring to the broader context (e.g., ethical considerations). Conclusions: This study contributes to the research gap in knowledge on the needs of professional caregivers to facilitate technology implementation among the population of older adults with cognitive decline. Future directions for research, practice, and policy are given, more specifically to improve knowledge among caregivers and on the development of decision support to retrieve safe and effective technologies that suit patient-centered 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".