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Record W3022572576 · doi:10.1177/1471301220922745

Using touchscreen tablets to support social connections and reduce responsive behaviours among people with dementia in care settings: A scoping review

2020· review· en· W3022572576 on OpenAlexafffund
Lillian Hung, Bryan Chow, John Shadarevian, Ryan O’Neill, Annette Berndt, Christine Wallsworth, Neil Horne, Mario Gregorio, Jim Mann, Cathy Son, Habib Chaudhury

Bibliographic record

VenueDementia · 2020
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsTrinity Western UniversityWestern UniversityUniversity of British ColumbiaSimon Fraser University
FundersAlzheimer Society
KeywordsDementiaTouchscreenPsychologyNursingMedical educationMedicineComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

The use of touchscreen tablets, such as the iPad, offers potential to support the person with dementia staying in a care setting, ranging from a long-term care home to an adult day programme. Although electronic devices are used among people with dementia, a comprehensive review of studies focusing on their impact and how they may be used effectively in care settings is lacking. We conducted a scoping review to summarize existing knowledge about the impact of touchscreen tablets in supporting social connections and reducing responsive behaviours of people with dementia in care settings. Our research team consists of patient partners and family partners, physicians, nurses, a medical student and an academic professor. A total of 17 articles were included in the review. Our analysis identified three ways in which touchscreen tablets support dementia care: (1) increased the person's engagement, (2) decreased responsive behaviours and (3) positive effect on enjoyment/quality of life for people with dementia. Lessons learned and barriers to the use of touchscreen tablets in the care of people with dementia are described. Overall, only a few studies delineated strategies that helped to overcome barriers to technology adoption in care settings. Knowledge translation studies are needed to identify effective processes and practical tips to overcome barriers and realize the potential of assistive technology in dementia care.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.416
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations53
Published2020
Admission routes2
Has abstractyes

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