Evaluating the Effectiveness of Online Training for a Comprehensive Violence Against Women Program: A Pilot Study
Bibliographic record
Abstract
Evaluating violence against women (VAW) training is essential to moving the field forward with proven approaches that can improve service provision for survivors of violence. Given existing resource constraints involved in VAW work, online training represents an economical and flexible option; however, existing evaluations of online programs in the VAW field are scant and face a variety of limitations. This study aimed to fill this gap by using a pre-/posttest design, comparison group, and mixed-method analysis to assess the effectiveness and value of an online training program. The program was intended to provide foundational knowledge in feminist antiviolence principles and values to a range of individuals working with survivors of intimate partner violence (IPV). Program participants ( N = 108) included volunteers, students, and professionals from various sectors, allowing for the application of the results to a broader field of VAW support services. This is important as individuals who work with IPV survivors may do so in a range of settings outside of the shelter context. Results demonstrate the potential for online VAW training to improve participants’ knowledge of and attitudes about VAW, which can positively inform their work with survivors. Qualitative responses provide further insight into course impact and highlight positive and negative aspects of the course. Although preliminary, these results provide justification for continued development and evaluation of online VAW training programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".