Building character strengths and virtues in Sri Lanka: a cluster randomized pre-post evaluation of a school-based intervention
Bibliographic record
Abstract
BACKGROUND: Sri Lanka (SL) is a multiethnic nation that has endured a decades-long civil war and a devastating tsunami. These events led to a widespread loss of life, displacement, destruction of family and social infrastructure, and economic collapse. Their impact was compounded by adverse social determinants such as poverty, unemployment, food insecurity, and homelessness. Sri Lankan youth are particularly vulnerable to the cumulative effects of these acute and chronic stressors, underscoring the urgent need for interventions that foster resilience and healing. METHODS: We will implement a 10-week school-based program - Leadership, Empathy, Altruism, Personal Growth, and Social Responsibility (LEAPS) - to cultivate character strengths in youth. The program will be culturally and historically tailored to the SL context, and will integrate key tenets from the country’s four major religions to promote unity, social values, and spiritual collaboration. LEAPS will consist of 10 interactive modules delivered through a web-based platform during regular school hours, supplementing the standard curriculum. Teachers will be trained using the train-the-trainer model to facilitate student engagement, ensure program fidelity and long-term sustainability. The impact of LEAPS on student character development and well-being will be evaluated using a cluster randomized pre-post design. Students will be assigned either to the intervention group (where they will participate in the LEAPS program alongside their regular curriculum) or the control group (where they will follow the standard curriculum without additional interventions). Assessments of character strengths and well-being will be conducted via student questionnaires at baseline, post-intervention, and at a 6-month follow-up. Changes over time and between groups will be analyzed to determine program benefits and effectiveness. DISCUSSION: The LEAPS program aims to enhance character development and well-being in SL youth. The interactive, web-based format of the program is also anticipated to facilitate uptake, knowledge translation, scalability and long-term sustainability, benefiting not only youth but also their families and broader communities. TRIAL REGISTRATION: This trial was registered with the Sri Lanka Trials Registry (No: SLCTR/2020/016) on June 25, 2020.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".