Reflections on facilitating teen dating violence prevention programming in schools during the COVID-19 pandemic: comparing online, in-person and hybrid facilitation
Bibliographic record
Abstract
This paper presents lessons learned from the development, adaptation and delivery of ‘It’s Complicated’, a grade 9 school-based teen dating violence prevention programme developed in Ontario, Canada, that addresses equity, diversity and emotional violence. Initially designed as an in-person intervention, the programme underwent adjustments for online and hybrid modalities due to COVID-19. This autoethnographic self-study reflects on implementing the curriculum over 269 workshops reaching 951 students. Delivery challenges and opportunities across three modalities – 180 in-person, 66 hybrid, and 23 online sessions – are examined. In-person facilitation proved advantageous for rapport-building and handling disclosures but posed challenges for anonymous student participation. Online teaching enhanced technological engagement but presented difficulties with privacy and extended time requirements. Hybrid delivery offered accessibility but faced challenges related to technological equity and classroom management. The study underscores the importance of teacher readiness, pedagogical alignment, and recognising the strengths and weaknesses of each modality. Technological equity and access issues were identified as central challenges, emphasising the necessity of optimal conditions for successful online and hybrid facilitation.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".