Training needs in dating violence prevention among school staff in Québec, Canada
Bibliographic record
Abstract
Introduction School staff play a central role in youth sexual health education (SHE), making them critical actors in dating violence (DV) prevention initiatives. However, since most school staff do not benefit from specific training on SHE, they often report feeling challenged in their roles as sex educators. The mention of a lack of self-efficacy to prevent DV is a concern as self-efficacy is associated with the motivation of adopting new behaviors. To optimize the scope of actions used to prevent DV, the SPARX program team sought to identify priority training needs using a mixed-methods design. Methods In the quantitative component of this study, 108 school staff completed an online survey regarding their sense of ease, self-efficacy and barriers faced in regard to DV prevention. For the qualitative component, 15 school staff participated in an individual semi-structured interview, sharing their experiences preventing DV. Descriptive analyses were conducted on the survey data, while direct content analysis using the self-efficacy theory concept was conducted on the interviews. Results To feel confident, school staff members need to learn about DV and healthy relationships and clarify their role in DV prevention. Turnkey activities, preformulated answers to adolescents’ questions, and strategies to reassure reluctant parents can strengthen staff’s sense of self-efficacy. Members of the school staff also want to feel supported and encouraged by their colleagues and school administration in their efforts to prevent DV. Discussion The results highlight the importance of providing training beyond acquisition of knowledge, which can improve attitudes toward DV prevention and a sense of self-efficacy used to transmit content and intervention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".