Building Resilience During COVID-19: Recommendations for Adapting the DREAM Program – Live Edition to an Online-Live Hybrid Model for In-Person and Virtual Classrooms
Bibliographic record
Abstract
of parents have reported mental health symptoms in their children. Specifically, depressive symptoms, anxiety, contamination obsessions, family well-being challenges, and behavioral concerns have emerged globally for children during the pandemic. Without treatment or prevention, such concerns may hinder positive development, personal life trajectory, academic success, and inhibit children from meeting their potential. A school-based resiliency program for children (DREAM) for children was developed, and the goal of this study was to collaborate with stakeholders to translate it into an online-live hybrid. Our team developed a methodology to do this based on Knowledge Translation-Integration (KTI), which incorporates stakeholder engagement throughout the entire research to action process. KTI aims to ensure that programs are acceptable, sustainable, feasible, and credible. Through collaboration with parents and school board members, qualitative themes of concerns, recommendations and validation were established, aiding in meaningful online-live translation. Even though the original program was developed for intellectually gifted children, who are at greater risk for mental health concerns, stakeholders suggested using the program for both gifted and non-gifted children, given the universal applicability of the tools, particularly during this pandemic time period when mental health promotion is most relevant. An online-live approach would allow students studying at home and those studying in the classroom to participate in the program. Broader implications of this study include critical recommendations for the development of both online-live school programs in general, as well as social-emotional literacy programs for children.
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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 | 0.039 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".