School-Based Parent Network Programs: A Partnership in Reducing Childhood Anxiety
Bibliographic record
Abstract
Today's world is quickly becoming full of distractions, busyness, and barriers that often force people to feel like they need to cope with life in isolation and feel disconnected from their community. Now more than ever, it is time to support and engage all stakeholders in a child's life to support their mental health well-being. Anxiety has become an all too common issue among school-aged children over the last decade. According to the Mental Health Commission of Canada (2021), 70 percent of people who live with mental illness see their symptoms begin before the age of 18, and it affects 1.2 million of our children and youth. That means about 1 in every 5 children experience mental disorders, of which anxiety is the most prevalent (Health Canada, 2021). It is clear from the research reviewed, such as in the study done by Caspe et al. (2006), that utilizing forms of connection and creating caring relationships plays a pivotal role in providing an environment for children where they are more likely to thrive and less likely to suffer from anxiety. By delving into the theoretical and research-based importance of building connections, and authentic relationships built on genuine care, we will see a tremendous opportunity to create a partnership between the two most significant environments in a child’s life and build a sense of community. This research will be used to propose a school-based program that is structured to support, network, and educate parents to feel empowered and become more confident in raising physically and mentally healthy children. The program's objective is to explore whether creating a caring and connected partnership between home and school can help reduce anxiety in school-aged children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".