Recommendations for Canada’s National Action Plan to End Gender-Based Violence: perspectives from leaders, service providers and survivors in Canada’s largest city during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The Canadian government has committed to a national action plan (NAP) to address violence against women (VAW). However, a formalized plan for implementation has not been published. Building on existing recommendations and consultations, we conducted the first formal and peer-reviewed qualitative analysis of the perspectives of leaders, service providers and survivors on what should be considered in Canada's NAP on VAW. METHODS: We applied thematic analysis to qualitative data from 18 staff working on VAW services (11 direct support, 7 in leadership roles) and 10 VAW survivor participants of a community-based study on VAW programming during the COVID-19 pandemic in the Greater Toronto Area (Ontario, Canada). RESULTS: We generated 12 recommendations for Canada's NAP on VAW, which we organized into four thematic areas: (1) invest into VAW services and crisis supports (e.g. strengthen referral mechanisms to VAW programming); (2) enhance structural supports (e.g. invest in the full housing continuum for VAW survivors); (3) develop coordinated systems (e.g. strengthen collaboration between health and VAW systems); and (4) implement and evaluate primary prevention strategies (e.g. conduct a gender-based and intersectional analysis of existing social and public policies). CONCLUSION: In this study, we developed, prioritized and nuanced recommendations for Canada's proposed NAP on VAW based on a rigorous analysis of the perspectives of VAW survivors and staff in Canada's largest city during the COVID-19 pandemic. An effective NAP will require investment in direct support organizations; equitable housing and other structural supports; strategic coordination of health, justice and social care systems; and primary prevention strategies, including gender transformative policy reform.
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 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 | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".