Examining organization and provider challenges with the adoption of virtual domestic violence and sexual assault interventions in Alberta, Canada, during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: In Canada, calls to domestic violence and sexual assault hotlines increased during the COVID-19 pandemic as stricter public health restrictions took effect in parts of the country. Moreover, the public health measures introduced to limit the transmission of COVID-19 saw many health providers abruptly pivot to providing services virtually, with little to no opportunity to plan for this switch. We carried out a qualitative research study to understand the resulting challenges experienced by providers of domestic violence and sexual assault support services. METHODS: Twenty-four semi-structured interviews were conducted to gather in-depth information from service providers and organizational leaders in the Canadian province of Alberta about the challenges they experienced adopting virtual and remote-based domestic violence and sexual assault interventions during the COVID-19 outbreak. Interview transcripts and field notes were analysed using a thematic analysis approach. RESULTS: Our findings highlighted multiple challenges organizations, service providers and clients experienced. These included: (1) systemic (macro-level) challenges pertaining to policies, legislation and funding availability, (2) organization and provider (meso-level) challenges related to adapting services and programmes online or for remote delivery and (3) provider perceptions of client (micro-level) challenges related to accessing virtual interventions. CONCLUSIONS: Equity-focused policy and intersectional and systemic action are needed to enhance delivery and access to virtual interventions and services for domestic violence and sexual assault clients.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".