THE USE OF ADMINISTRATIVE DATA TO STUDY THE TRIPLE P – POSITIVE PARENTING PROGRAM
Bibliographic record
Abstract
This study is among the first Canadian population-based evaluations designed to examine associations of the Triple P – Positive Parenting Program (Triple P) for mother and child outcomes at the community level. Uniquely, this study was conducted independently of program implementation, using data collected for other purposes. Three anonymized British Columbia provincial administrative data sources were used, in addition to program data collected by administrators (Island Health). The study employed a quasi-experimental design to examine benefits of Triple P at the community level, and used sociodemographic community characteristics to match 11 target communities where Triple P was implemented with comparison communities where Triple P was not implemented. The study’s design and analyses took into account the phased-in implementation of Triple P across Vancouver Island (2004–2008), drawing on pre- and post-implementation data for all of the studied communities. Hierarchical linear modeling results showed that children living in communities where the program had been administered were more likely to have been diagnosed with conduct disorders and to have used counseling services. Program intensity was not associated with any of the child health outcomes. For mothers, higher program intensity was associated with lower odds of being diagnosed with mental health conditions. Future research should continue to demonstrate the feasibility of a population-based approach and the use of secondary data along with program data to examine community-based intervention programs.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".