AssessBlocks
Bibliographic record
Abstract
Natural disasters cause long-lasting mental health problems such as PTSD in children. Following the 2011 Earthquake and Tsunami in Japan, we witnessed a shift of toy block play behavior in young children who suffered from stress after the disaster. The behavior reflected their emotional responses to the traumatic event. In this paper, we explore the feasibility of using data captured from block-play to assess children's stress after a major natural disaster. We prototyped sets of sensor-embedded toy blocks, AssessBlocks, that automate quantitative play data acquisition. During a three-year period, the blocks were dispatched to fifty-two post-disaster children. Within a free play session, we captured block features, a child's playing behavior, and stress evaluated by several methods. The result from our analysis reveal correlations between block play features and stress measurements and show initial promise of using the effectiveness of using AssessBlocks to assess children's stress after a disaster. We provide detailed insights into the potential as well as the challenges of our approach and unique conditions. From these insights we summarize guidelines for future research in automated play assessment systems that support children's mental health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.133 | 0.068 |
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".