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Record W2104210542

Effects of improvement on selective attention: Developing appropriate somatosensory video game interventions for institutional-dwelling elderly with disabilities

2012· article· en· W2104210542 on OpenAlexaff
Shang-Ti Chen, I-Tsun Chiang, Eric Zhi Feng Liu, Maiga Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)PsychologySomatosensory systemVideo gameQuality of life (healthcare)AudiologyPhysical therapyPhysical medicine and rehabilitationGerontologyMedicineMultimediaComputer sciencePsychiatryPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to develop appropriate somatosensory video game interventions on enhancing selective attention of institutional-dwelling elderly with disabilities. Fifty-eight participants aged 65~92 were recruited and divided into four groups, 4-week and 8-week experimental and two control groups, for evaluating the one-month carry-forward effects by Vienna Test System. Fourteen participants in experimental groups voluntarily completed 30-minute Xbox games 3 times per week for a total of 4 and 8 weeks. The results showed that: (1) except sum of incorrect reaction, a majority of participants whose selective attentions had significant improvements in immediate effect, carry-forward effects and overall effect in 8-week group (p <.05); (2) 5 out of 8 items in selective attention tests had significant immediate and carry-forward effects and one overall effect in 4-week intervention (p <.05) and (3) The results conclude that using somatosensory video games is a viable approach to promote selective attention of institutional-dwelling elderly with disabilities. The present study also found that this approach could motivate elderly to participate with a variety of sound, music and sensory stimulations and is a viable and valuable direction to promote quality of life in long-term care system.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.448

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)0.0000.000

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 head0.030
GPT teacher head0.305
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations19
Published2012
Admission routes1
Has abstractyes

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