Improving the implementation of KiVa antibullying program with tailored support: Study protocol for a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: There are no evidence-based models to support the implementation of school-based bullying prevention programs. Our primary objective is to examine the impact of tailored support on the implementation of the KiVa antibullying program. Our second objective is to evaluate whether the offered support influences student outcomes (e.g., victimization, bullying perpetration). We also assess the cost-effectiveness of the provided support and conduct a process evaluation. METHODS: In a cluster randomized control trial (cRCT), we compare program fidelity between schools that receive implementation support and those that do not. Twenty-four (N = 24) schools in Finland were randomized to either the IMPRES condition (receiving support, n = 12) or the control group (KiVa as usual, n = 12). In the IMPRES condition, pre-assessment and staff training were organized, and a selected team of staff members received four mentoring sessions during one academic year. Staff and students answer questionnaires at the end of school year 0, at post-intervention (year 1) and again at the 1-year follow-up (year 2). Our primary outcomes concern two main program components - universal and indicated actions - reflecting program fidelity. As secondary outcomes, we examine the level of bullying victimization and perpetration as well as students' perception of several program fidelity indicators. Finally, we assess several tertiary outcomes, collect resource data and conduct qualitative interviews to perform additional analyses. CONCLUSION: This trial will inform us of whether implementation support can boost program fidelity and have a distal impact on bullying prevalence. TRIAL REGISTRATION: ISRCTN15558617 https://doi.org/10.1186/ISRCTN15558617.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.093 | 0.013 |
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".