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Record W4400405715 · doi:10.1080/01490400.2024.2373415

Playing Board Games to Increase Emotional Competencies in School-Age Children and Older People: A Systematic Review

2024· review· en· W4400405715 on OpenAlexaff
Pierre Cès, Mathilde Duflos, Élodie Tricard, Sandra Jhean‐Larose, Caroline Giraudeau

Bibliographic record

VenueLeisure Sciences · 2024
Typereview
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of British Columbia
FundersConseil Régional du Centre-Val de Loire
KeywordsPsychologyDevelopmental psychologyOlder peopleApplied psychologyMedical educationGerontologyMedicine

Abstract

fetched live from OpenAlex

Board games have always been a part of our lives. Most studies highlight their potential for learning in childhood and for maintaining cognitive functions in older people. Nevertheless, regarding the benefits in childhood and older people and the social nature of board games, few studies have investigated the emotional nature of these shared play sessions. Thus, we conducted a systematic review of the scientific literature between 2011 and 2024 to identify the benefits of playing board games for both school-age children and older people in terms of emotional competence. First, we collected 1995 articles with defined keywords from online databases. Then, 11 articles were selected. For older people, most studies reported the usefulness of board games for emotional competencies, especially regarding depression, social interactions and communication. In childhood, board games were found to be an interesting tool for emotional competencies, helping reduce problem behaviors, support pro-social behaviors and emotion-related discussion.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.362
Teacher spread0.315 · 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 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

Citations8
Published2024
Admission routes1
Has abstractyes

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