Building capacity in Alberta to prevent domestic violence: Results from a community of practice project
Bibliographic record
Abstract
In 2011, Shift: The Project to End Domestic Violence entered into a formal partnership with the Government of Alberta to rebuild the recently released (November 2013) family violence prevention framework. Both partners agreed on the importance of ensuring that the research on which the prevention framework was based was accessible for practitioners, service providers, policy makers and system leaders throughout the province. Shift also realized that local communities would need to develop the capacity to implement the primary prevention strategies being proposed in the new provincial prevention strategy. As a result, Shift explored evidenced-informed models on knowledge translation, mobilization, and integration and (through a SSHRC Partnership grant and Canadian Women’s Foundation grant) engaged in a pilot project to test a particular Community of Practice (CoP) model in two regions in Alberta. The CoP model piloted was designed with the understanding that the best way to build community capacity for domestic violence primary prevention would be to work through the current research and support practitioners and system leaders to understand how it applies to their local context and communities. We believed this approach would support changes, not only at the individual practitioner level, but also support shifts in decision-making at the organizational, systems, and policy level.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".