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A Web and Nonimmersive VR Approach for Reminiscence Therapy: A Caregiver Perspective Comparison

2022· article· en· W4312848693 on OpenAlexaff
Winnie Sun, Rabia Akhter, Álvaro Uribe-Quevedo, Daniel Presas, Ramiro Liscano, Sheri Horsburgh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsOntario Shores Centre for Mental Health SciencesOntario Tech University
Fundersnot available
KeywordsReminiscenceVirtual realityPerspective (graphical)PsychologyDistancingDementiaMusic therapyComputer sciencePsychotherapistHuman–computer interactionMultimediaCognitive psychologyMedicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Reminiscence therapy (RT) is a multi-sensory treatment that uses a combination of sight, touch, taste, smell, and sound to help persons with dementia (PWD) remember events, people, and places from their past lives. Currently, RT relies on in-person sessions where albums, memorabilia, and media such as pictures and music are brought by the caregivers. However, such sessions have been negatively impacted by physical distancing and restricted access to institutionalized facilities with the goal of reducing exposure and potential outbreaks. Due to such restrictions, digital technologies such as mobile applications and immersive solutions including virtual and augmented reality, have started gaining momentum as supplementary tools for RT. This paper presents a caregiver perspective comparative study between a web RT application and its nonimmersive Virtual Reality counterpart to understand the limitations and opportunities both platforms present for facilitating engaging experiences for people with dementia towards recalling memories while easing the therapy process for the caregivers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.348
Teacher spread0.300 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes1
Has abstractyes

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