Supporting victims of domestic violence in rural and island communities during COVID-19: the impact of the pandemic on service providers in North East Scotland and Orkney
Bibliographic record
Abstract
We investigate the impact of the COVID-19 pandemic on domestic violence service providers in rural and island communities in North East Scotland and Orkney. Domestic abuse and violence in rural areas is typically underestimated and might be more hidden due to stigma, a surveillance culture, and the practical difficulties of accessing services. The geographical challenges of rural and remote areas in relation to domestic violence are, to some extent, further amplified in small island locations, given population sizes, terrain and separation by sea. In such communities, visits to a service organisation’s offices, or a visit by one of their staff, might publicly mark a service user out as a domestic abuse survivor. This article focuses on the move to digital and telephone provision of support in areas where broadband internet access is inconsistent and service users may live many miles from sources of support. At the same time, the move to online modes of communication was welcomed by staff in relation to offering opportunities for training and networking. There was also use of social and local media to raise awareness of the prevalence of domestic violence in these locations and to counter the myth of idyllic and abuse-free rural and island communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".