Feasibility and Acceptability of a Dance Fundamental Movement Skill Intervention for Symptoms of Depression and Anxiety in Children
Bibliographic record
Abstract
Anxiety and depression are highly prevalent mental health conditions in children. Rates of both disorders are rising, surpassing the capacity of available treatment services (albertapatients, 2022; Edwardson, 2022; Statistics Canada, 2020). Lack of treatment can often result in worsening of symptoms towards serious long-term effects (Centers for Disease Control and Prevention, 2023). Additional treatment supports must be made available. The goal of this doctoral research is to examine the feasibility and acceptability of genre-inclusive dance as a psychosocial intervention for anxiety and depression in children. The first manuscript of this doctoral work shares lessons learned in a program evaluation using the Footprints Movement Tool (FMT) as an after-school dance class for elementary school children. The subsequent mixed-methods pilot study used the FMT as a 14-week intervention for children (aged 8 to 10 years) displaying symptoms of depression and/or anxiety disorders in screening results of the Revised Children’s Anxiety and Depression Scale, parent report. The quantitative results for feasibility, acceptability, and preliminary efficacy of the FMT are discussed in Manuscript 2, with participant experiences and stakeholder perspectives discussed qualitatively in Manuscript 3. Challenges with conducting this work post-COVID-19 are discussed in Manuscript 3 using interviews conducted with other community dance providers, with strategies proposed for future success. The intervention was acceptable for all stakeholders. It showed promising results reducing parent-reported symptoms of anxiety and depression, and thus merits further exploration. Study findings indicate that this intervention could be a feasible and viable method of significantly improving children’s mental health.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".