Effects of a positive discipline program on parenting outcomes
Bibliographic record
Abstract
BACKGROUND: Parental use of punishment remains a significant concern considering its prevalence and negative impacts on children's well-being. Positive Discipline in Everyday Parenting (PDEP) is a group-based program aimed at helping parents shift away from the use of punitive parenting practices toward positive discipline. OBJECTIVE: This quasi-experimental PDEP evaluation examined self-reported parenting practices in a Canadian sample of 183 parents of children aged 2-6 years. PARTICIPANTS AND SETTING: There were 101 parents in the experimental condition, and 82 parents in the wait-list comparison group. METHODS: Data were collected as part of a larger Canadian project spanning five data collection cycles (2018-2019 until 2023-2024) and gathered information on PDEP outcomes through a variety of methods. RESULTS: Compared with the wait-list group, parents who completed PDEP reported a statistically significant decrease in physical punishment use (e.g., spanking) and emotional punishment (e.g., making child sit alone in corner or another room; taking away an activity), and an increase in proactive parenting (e.g., preparing child for an activity; explaining the reason for a request) from pre- to post-program and from pre-program to 1-month follow-up. The findings represented small (emotional punishment) and large (physical punishment, proactive parenting) effect sizes. CONCLUSIONS: This first experimental evaluation of PDEP, which is fundamentally different from behavior management interventions, indicates that the program was effective in reducing parental use of punishment-based practices and increasing proactive parenting. Additional research is warranted that uses a fully randomized design and that examines parenting effects in ways that complement self-report measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".