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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".