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Record W4403424335 · doi:10.1145/3677095

Playful Resilience: Empowering Recovery through Autobiographical Game-Based Storytelling in the Opioid Epidemic

2024· article· en· W4403424335 on OpenAlexaffabout
Sandra Danilovic, Kenny Chee, Michelle Skop

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of TorontoWilfrid Laurier University
FundersUniversitas Brawijaya
KeywordsStorytellingNarrativePsychological resiliencePsychologyAddictionSociologySocial psychologyPsychiatryArt

Abstract

fetched live from OpenAlex

The opioid epidemic is a persistent public health problem throughout Canada, with opioid-related deaths spiking during the COVID-19 pandemic. Focusing on Brantford and Hamilton, Ontario, which have high rates of opioid poisoning, this participatory game jam project examines how 21 adults (ages 18+) with a history of opioid addiction make sense of their life experiences through the processes of autobiographical game-based storytelling, including ideation, narrative design, and environmental storytelling. Phenomenological interviews, processual artifacts like concept sketches, doodles, and reflections, and game prototypes generated through Scratch, Twine, and Bitsy facilitated the exploration of six key phenomena representing autobiographical game-based storytelling: maze metaphors, decision-making, compact games, morality and religion, resilience, and social communion. This interdisciplinary project explores how game-based storytelling supports recovery and restores dignity among adults experiencing opioid addiction while raising awareness of health inequities, thereby humanizing the opioid crisis.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.428
Teacher spread0.337 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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