Using Integral Theory to Study the Effectiveness of HeartMath Biofeedback and Social-Emotional Learning in Adolescent Emotion Regulation
Bibliographic record
Abstract
The purpose of this study was to address the effectiveness of teaching social-emotional learning (SEL) and mindfulness with biofeedback to help Grade-9 students manage and regulate stress and anxiety. As a classroom teacher, I have been noticing a recent uptick in internalizing (withdrawal and avoidance) and externalizing (outbursts and aggression) student behaviours within the classroom and hallways. Students described being stressed, and uncertain, with many lacking self-confidence and emotional regulation skills. While rates of adolescent stress and anxiety have been slowly increasing (CAMH, 2021), the COVID-19 pandemic has since contributed to even higher stress, greater depression, and increased loneliness in adolescents (Ellis et al., 2020). Additionally, students transitioning from middle school to high school often experience additional stress and worry, which, if not addressed, can lead to further downstream negative mental and emotional effects (Evans et al., 2018)
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".