The Last Straw!: A Tool for Participatory Education About the Social Determinants of Health
Bibliographic record
Abstract
BACKGROUND: In response to a scarcity of teaching tools regarding the social determinants of health (SDOH), Kate Reeve and Kate Rossiter created The Last Straw! board game, an innovative participatory education tool to facilitate and engage critical thinking about the SDOH. OBJECTIVES: The Last Straw! is designed to encourage discussion about the SDOH, promote critical thinking, and build empathy with marginalized people. METHODS: The game begins as each player rolls the dice to create a character profile, including socioeconomic status (SES), race, and gender. Based on this profile, players then receive a certain number of "vitality chips." Moving across the board, players encounter scenarios that cause them to gain and lose chips based on their profile. The player who finishes the game with the most chips wins the game. The game can be facilitated for a variety of audiences, including both players with no prior knowledge of the SDOH and those experienced in the field. CONCLUSIONS: The game has been played with students, policymakers, and community workers, among others, and has been met with immense enthusiasm. Here, we detail the game's reception within the community, including benefits, limitations, and next steps.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".