Navigating multiple pandemics: A critical analysis of the impact of COVID-19 policy responses on gender-based violence services
Bibliographic record
Abstract
COVID-19 illustrated what governments can do to mobilise against a global threat. Despite the strong governmental response to COVID-19 in Canada, another 'pandemic', gender-based violence (GBV), has been causing grave harm with generally insufficient policy responses. Using interpretive description methodology, 26 interviews were conducted with shelter staff and 5 focus groups with 24 executive directors (EDs) from GBV service organizations in Ontario, Canada. Five main themes were identified and explored, namely that: (1) there are in fact four pandemics at play; (2) the interplay of pandemics amplified existing systemic weaknesses; (3) the key role of informal partnerships and community support, (4) temporary changes in patterns of funding allocation; and (5) exhaustion as a consequence of addressing multiple and concurrent pandemics. Implications and recommendations for researchers, policy makers, and the GBV sector are discussed.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| 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".