A Story Grows in Rural Uganda: Studying the Effectiveness of the Storytelling/Story-Acting (STSA) Play Intervention on Ugandan Preschoolers’ School Readiness Skills
Bibliographic record
Abstract
Children in the developing world are at far greater risk for emotional, psychological, and health challenges; at the same time, they have little access to clinical interventions or other support services. The intervention presented in this study is a low-cost, play-based intervention that we believed could help to address early learning and developmental challenges in preschool children in under-resourced areas, in this case, rural Uganda. This study explores the connection among storytelling, story-acting, and school readiness skills, which include emergent literacy, receptive vocabulary, and theory of mind. Ugandan children ages 3 to 5 were randomly assigned to participate in either the Storytelling/Story-Acting (STSA) play intervention (n = 63) or a story-reading activity (n = 60) for one hour twice per week for six months. With the aid of translators, all children were assessed for school readiness skills (emergent literacy, receptive vocabulary, and theory of mind) before and after the six-month intervention. Caregivers were also administered an interview that assessed their educational level, quality of life, reading aloud to target child, social support, and total possessions. Overall, participants benefited significantly from a story-reading activity with or without STSA. When examining both groups together (N = 121 post-intervention), school readiness skills significantly improved. Caregiver variables also predicted these three child outcome variables at baseline, suggesting that caregivers play a significant role in the development of their children’s school readiness skills. Implications for these findings are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".