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Record W2671242022 · doi:10.1089/g4h.2016.0037

A Questionnaire-Based Study on the Perceptions of Canadian Seniors About Cognitive, Social, and Psychological Benefits of Digital Games

2017· article· en· W2671242022 on OpenAlexafffundabout
Emmanuel Duplàa, David Kaufman, Louise Sauvé, Lise Renaud

Bibliographic record

VenueGames for Health Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerceptionPsychologyCognitionApplied psychologySocial psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study explored the perceptions of seniors who play digital games on the potential benefits of these games and on the factors associated with these perceptions. MATERIALS AND METHODS: We developed and administered a questionnaire to a sample of 590 Canadian seniors in British Columbia and Quebec that addressed demographics, digital game practices, and perceived benefits. RESULTS: Results of administering the questionnaire showed that cognitive benefits were reported more frequently than social or psychological benefits. First language and gender were associated with the benefits reported, with fewer Francophones and women reporting benefits. The most important factor found was whether or not they played online, as playing online was associated with greater perceptions of social, as well as cognitive, benefits. CONCLUSION: Social and cognitive benefits are reported by seniors from playing digital games and should be investigated through future experimental and quasi-experimental research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.403
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2017
Admission routes3
Has abstractyes

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