The use of technology by seniors with neurocognitive disorders in long-term care: a scoping review
Bibliographic record
Abstract
BACKGROUND: To map the current state of knowledge about the use of technology with seniors with neurocognitive disorders in long-term care to foster interactions, wellness, and stimulation. METHODS: Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus); MEDLINE; PsycINFO; Embase and Web of Science were searched in eligible literature, with no limit of time, to describe the current use of technology by seniors with neurocognitive disorders in long-term care. All types of literature were considered except for theses, editorial, social media. This scoping review was built around the recommendations of Peters et al. (2020 version). Three researchers collaborated on the selection of articles and independently reviewed the papers, based on the eligibility criteria and review questions. RESULTS: The search yielded 3,605 studies, of which 39 were included. Most technology type reported was robotics. Included studies reports different positive effects on the use of such technology such as increase of engagement and positive. CONCLUSION: The study highlights different types and potential benefits of technology for long-term care residents with neurocognitive disorders, emphasizing the crucial need for additional research to refine interventions and their use.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".