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Record W4403307386 · doi:10.1145/3665463.3678839

Mindfulness Techniques Taught Through Game Mechanics

2024· article· en· W4403307386 on OpenAlexaff
D. Hwang, Edward F. Melcer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCarleton University
Fundersnot available
KeywordsMindfulnessComputer scienceCognitive sciencePsychologyPsychotherapist

Abstract

fetched live from OpenAlex

The importance of coping with stress for adolescents is well documented. Various approaches to providing stress-coping strategies have been proposed, and mindfulness has been one of the commonly introduced techniques. It has shown clinical evidence of effectiveness, and questionnaires measuring its efficiency have been developed as well. However, the majority of current methods take a conventional approach of involving verbal or text-based explanations of mindfulness practices, which suffer from a distinct lack of engagement and long-term adherence. However, there are still limited methods for introducing mindfulness to adolescents. In order to make mindfulness not only more accessible for teens but also more engaging and effective, we introduce Mindful Fido, an interactive narrative empathy game that involves players in three different mindfulness techniques, making the learning process more enjoyable and relatable to their daily experiences. In Mindful Fido, players follow the story of a teenage student who faces common stress factors of adolescence. The game is designed to keep players engaged and present mindfulness techniques as game mechanics that naturally allow players to practice mindfulness.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.034
GPT teacher head0.355
Teacher spread0.321 · 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 designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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