Preventing Punitive Violence: Implementing Positive Discipline in Everyday Parenting (PDEP) with Marginalized Populations in Bangladesh
Bibliographic record
Abstract
Physical and other types of punishment remain common in Bangladesh, despite overwhelming evidence of their harm and worldwide efforts to decrease their use. One of the strategic priorities of Save the Children in Bangladesh's Child Protection Program is to protect children from physical and humiliating punishment in homes, schools, and other settings. Save the Children in Bangladesh selected the Positive Discipline in Everyday Parenting (PDEP) Program to provide parents with alternatives to physical punishment that comply with human rights standards while strengthening relationships and understanding of child development. High-risk communities where children are particularly vulnerable were selected for this project. The PDEP program was delivered to 857 parents living in lower socioeconomic areas of Bangladesh, including ethnic minority groups, and parents living in urban slums of Dhaka and rural brothel areas. Due to the low levels of education of the participants (almost two-thirds of participants had not completed elementary school), simplified pre and posttests were utilized. Following program completion, parents' approval of both physical punishment and punishment in general declined; they were less likely to view typical parent-child conflicts as intentional misbehavior and were less reactive to frustration. In addition, parents indicated an increased understanding of the positive discipline and more confidence in their parenting skills. Before taking PDEP, 64% of the parents often felt like they just did not know what to do as a parent, compared to 34% following program completion. PDEP demonstrated the potential to decrease the use of physical and humiliating punishments by parents living in high-risk communities in Bangladesh.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".