Playful Resilience: Empowering Recovery through Autobiographical Game-Based Storytelling in the Opioid Epidemic
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".