Preventing Domestic Violence in Alberta: A Cost Savings Perspective
Bibliographic record
Abstract
Recent studies show that Alberta has the fifth highest rate of police reported intimate partner violence and the second highest rate of self reported spousal violence in Canada, and despite a 2.3 percent decline over the last decade, the province’s rate of self-reported domestic violence has stubbornly remained among the highest in Canada; rates of violence against women alone are 2.3 percentage points higher than the national average. In fact, every hour of every day, a woman in Alberta will undergo some form of interpersonal violence from an ex-partner or ex-spouse. Besides the devastating toll that domestic violence has on victims and their families, the ongoing cost to Albertans is significant. In the past five years alone it is estimated that over $600 million will have been spent on the provision of a few basic health and non health supports and that the majority of this cost ($521 million) is coming out of the pockets of Albertans in the form of tax dollars directed at the provision of services. Fortunately, investment in quality prevention and intervention initiatives can be very cost effective, returning as much as $20 for every dollar invested. Recent research on preventative programming in the context of domestic violence shows promising results in reducing incidents of self-reported domestic violence. The economic analysis of this preventative programming suggests that the benefits of providing the various types of programming outweighed the costs by as much as 6:1. The potential cost savings for the Alberta context are significant; the implementation of these preventative programs has been estimated to be approximately $9.6 million while generating net cost-benefits of over $54 million. Domestic violence is a persistent blight, and continues to have a significant impact on individuals and families in Alberta, but potent tools exist to fight it. This brief paper offers a cogent summary of its costs, and the benefits that could be reaped by investing in quality prevention and intervention programs, making it essential reading for policymakers and anyone else prepared to use them.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".