Partners in Parenting: A Multi-Level Web-Based Approach to Support Parents in Prevention and Early Intervention for Adolescent Depression and Anxiety
Bibliographic record
Abstract
Depression and anxiety disorders in young people are a global health concern. Various risk and protective factors for these disorders are potentially modifiable by parents, underscoring the important role parents play in reducing the risk and impact of these disorders in their adolescent children. However, cost-effective, evidence-based interventions for parents that can be widely disseminated are lacking. In this paper, we propose a multi-level public health approach involving a Web-based parenting intervention, Partners in Parenting (PIP). We describe the components of the Web-based intervention and how each component was developed. Development of the intervention was guided by principles of the persuasive systems design model to maximize parental engagement and adherence. A consumer-engagement approach was used, including consultation with parents and adolescents about the content and presentation of the intervention. The PIP intervention can be used at varying levels of intensity to tailor to the different needs of parents across the population. Challenges and opportunities for the use of the intervention are discussed. The PIP Web-based intervention was developed to address the dearth of evidence-based resources to support parents in their important role in their adolescents' mental health. The proposed public health approach utilizes this intervention at varying levels of intensity based on parents' needs. Evaluation of each separate level of the model is ongoing. Further evaluation of the whole approach is required to assess the utility of the intervention as a public health approach, as well as its broader effects on adolescent functioning and socioeconomic outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".