iCOPE with COVID-19: A Brief Telemental Health Intervention for Children and Adolescents during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has prompted unprecedented disruptions to the daily lives of children and adolescents worldwide, which has been associated with an increase of anxiety and depressive symptoms in youth. However, due to public health measures, in-person psychosocial care has been affected causing barriers to mental health care access. This study investigated the feasibility, acceptability and preliminary effectiveness of iCOPE with COVID-19, a brief telemental health intervention for children and adolescents to address anxiety symptoms. Sessions were provided exclusively using videoconferencing technology. Feasibility and acceptability were measured with client satisfaction data. The main outcome measure for effectiveness was anxiety symptom severity measured using the Screen for Child Anxiety and Related Disorders (SCARED). Results indicated that the treatment was well accepted by participants. Significant reductions in anxiety were noted for social anxiety, and were observed to be trending towards a mean decrease for total anxiety. The findings suggest that this brief telemental health intervention focused on reducing anxiety related to COVID-19 is acceptable and feasible to children and adolescents. Future research using a large sample and with a longer follow-up period could inform whether symptom decreases are sustained over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".