Acceptability of A Web-based Character Strengths Module for Early Adolescent School Children
Bibliographic record
Abstract
Background: Frequent socio-political conflicts in the past decades have adversely affected the well-being of youth in Sri Lanka. There is a need to support youth development, mitigate past trauma, and promote social harmony. School-based interventions to improve character development, emotional regulation, and tolerance have good evidence and may benefit adolescents in Sri Lanka. Our objective was to develop a novel character strength program in Sinhala to promote Leadership, Empathy, Altruism, Personal Growth, and Social Responsibility (LEAPS). This program consists of ten web-based modules and is facilitated by teachers. This initial evaluation focuses on the first module and its acceptability among students and teachers. Method: Program modules were developed by a child and adolescent psychiatrist with the support of mental health and educational experts. The first module was piloted among grade 8 students (12-14 years) and teachers of two schools in the Gampaha District. It was designed to be interactive and contains various cultural activities and questions. On completion, participants gave feedback online via Likert scales. Ethical approval for the study was obtained from the Faculty of Medicine, University of Kelaniya. Results: A total of 115 students and 66 teachers participated. Overall, more than 95% of students and teachers found the module appropriate and suitable. The students had more favourable ratings for the module compared to the teachers on aspects of understanding the content (p<0.001), ability to complete by self (p<0.001), suitability to age (p<0.001), shorter time for completion (p<0.001) and suitability of the design (p<0.001). Both groups suggested that the pictures and activities be increased. Conclusions: Teaching character strengths using a web-based intervention was well accepted by adolescent students and teachers in Sri Lanka.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".