Development and Feasibility of an Online Brief Emotion Regulation Training (BERT) Program for Emerging Adults
Bibliographic record
Abstract
Mental wellness is a critical component of healthy development in emerging adulthood and serves to protect against stress and promote resilience against psychopathology. Emotion regulation is a key mechanism for effective prevention because of its role in socio-emotional competence and its transdiagnostic significance for psychopathology. In this feasibility study, a brief, time and cost-effective emotion regulation training program for emerging adults (BERT) was developed and tested using the RE-AIM framework. Importantly, building interventions within the context of an implementation framework, such as the RE-AIM framework, enhances the chances that an intervention will be able to scale out and scale up. First, the brainwriting premortem method was utilized to refine program content, conducting focus groups a priori to identify potential program failures prior to program implementation. Undergraduate students (n = 12) attended four focus groups presenting initial program content. Four clinicians were also interviewed to determine program barriers. Qualitative analyses aggregated participant feedback to identify compliments, changes, and concerns about BERT and critical feedback was immediately implemented prior to initial testing. BERT was rooted in cognitive-behavioral practices and informed by the Gross model of emotion regulation. The 5-week program was then examined in a college sample (N = 42) to evaluate implementation (low attrition, high content engagement, favorable attitudes, low incidence of technical errors, costs), reach (enrollment and completion demographics comparable to the population in which recruitment took place), and efficacy (positive change in emotion regulation pre- to post-program). Of the recruited participants, 36 remained in the study where 27 completed at least 80% of program content. Repeated-measures ANOVAs exhibited significant improvements in emotion regulation, psychological distress, and negative affectivity, suggesting promising initial efficacy. Initial data provide support for feasibility and a future randomized control trial. BERT has potential significance for promoting healthy development as its brief electronic format reduced barriers and the program development process incorporated stakeholder feedback at multiple levels to inform better implementation and dissemination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".