Co-designing a gamified learning tool to explore the effectiveness of social prescribing among healthcare providers
Bibliographic record
Abstract
Background: Healthcare is a complex systemic response that addresses the acute health issues of individuals and prevents the downstream consequences of illnesses. With costs and demands on this system rising, social prescribing has emerged a viable and asset-based upstream intervention that decreases social isolation and repeat visits to primary care. The uptake of social prescribing in Canada is slow and requires a creative engagement strategy amongst healthcare workforces. A participatory process was explored to engage healthcare providers iteratively to co-design a gamified learning tool on social prescribing. \n \nMethods: Working with social prescribing champions in Ontario, the project team developed an exploratory gameboard design that highlighted the impact of social prescribing on communities. Informed by several co-design workshops involving the principal team, systemic design mapping tools were utilized; iterative inquiry, journey mapping, rich context, actors map, transition by design and three horizons. Patient stories and case studies from local and regional health institutions aided in the development of game personas. The next phase of the project will involve the board piloted in various settings in with objective knowledge assessment and subjective feedback questionnaires being administered. \n \nAnalysis: Initial analysis of case studies and client journeys developed personas that reflect the needs of members of equity owed groups; racialized folks, substance users, gender nonbinary folks and those living with chronic diseases. Gaming artefacts were developed through team dialogue and weekly brainstorming exercises where the following needs emerged: language that was person centered, combined probabilities (using nonconventional dice, a spinner and gaming personas), and narratives of lived experiences. Data on participant learning, and user experience feedback will emerge with the next phase of the project. Initial findings included the need for representation of seniors and older adults. \n \nConclusions: Peer-based learning on social determinants and social prescribing have been demonstrated during pilot games. Next phase will establish practicality for the tool’s integration in medical educational and multidisciplinary learning. Increased social prescribing maybe achieved through game play that is participatory like this. \n \nKeywords: Social Prescribing, Medical Education, Gamification, Seniors
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".