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Record W3012299423 · doi:10.1007/s40869-020-00103-7

For Whom the Games Toll: A Qualitative and Intergenerational Evaluation of What is Serious in Games for Older Adults

2020· article· en· W3012299423 on OpenAlexafffund
Najmeh Khalili‐Mahani, Bob De Schutter, Mahsa Mirgholami, Rebecca Goodine, Scott DeJong, Roseleen McGaw, Sue Meyer, Kim Sawchuk

Bibliographic record

VenueThe Computer Games Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsConcordia University
FundersConcordia University
KeywordsPsychologyFocus groupDistractionExploratory researchCognitionQualitative researchSocial psychologyApplied psychologySociologyCognitive psychology

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to engage older adults in discussions about digital serious games. Using a qualitative exploratory approach, we report observations from more than 100 h of conversations with individuals in the age range 65–90, in a study entitled “Finding better games for older adults” (June 2017–December 2019). Phase 1 (19 older participants, 3 young research students) involved conversations around a quantitative study of cognitive benefits of digital playing (minimum 6 h/person). Phases 2 and 3 involved a focus group in the form of a community class (10 weeks, 2 h per meeting), involving introduction to digital game genres, playing, and discussing motivations and obstacles for current and future play. Cognitive stimulation, emotional distraction and physical therapy were initially stated as the motives for game play. However, with growing familiarity and voluntary exchanges of personal stories between older and younger participants, the cultural significance of the medium of game (especially with story-telling and VR technology) became more important to older adults. More than mechanical inaccessibility, lack of access to the cultural discourse about games presents barriers for older adults. To create a safe, comfortable and accessible space for intergenerational learning and play is of primary importance both for users and designers, should serious games be considered for the future of digital 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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.049
GPT teacher head0.371
Teacher spread0.322 · 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 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

Citations27
Published2020
Admission routes2
Has abstractyes

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