Changes in Anxiety Symptoms and Their Correlates in Adolescents Participating in a School-Based Anxiety Prevention Program During the COVID-19 Pandemic
Bibliographic record
Abstract
Anxiety disorders have been on the rise among adolescents over the past decade. The COVID-19 pandemic appears to have contributed to this increase, putting further pressure on often already overburdened health systems. Universal prevention programs may offer a potential solution, but few have been evaluated in the context of a pandemic. The objective of this article is to measure the impact of a universal prevention program—the HORS-PISTE program—on several anxiety-related variables in the context of a pandemic. The HORS-PISTE program consists of 10 workshops spread over the two first years of high school, secondary 1 and 2 (grade 7 and 8 equivalent). Workshops are held in a classroom setting and focus on the development of psychosocial skills. The study was conducted in Quebec with 1,202 secondary 1 and 2 students (48.7% girls, 51.3% boys) with an average age of 12.58 years ( SD = 0.75). They completed an assessment protocol before and after participating in the HORS-PISTE program in the autumn of 2020. Their answers were subjected to descriptive analysis and multivariate analysis of variance. Results indicate a significant decrease in symptoms for several of the measured variables between the two measurement times, such as those associated with panic disorder, generalized anxiety disorder, and test anxiety. The results also show a decrease in some variables related to the interference of anxiety symptoms and the cognitive and behavioral vulnerabilities targeted by the program. The discussion highlights possible explanations for the results, as well as how universal prevention programs may contribute to the prevention of anxiety during adolescence, especially in a pandemic context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".