Delivery of Distance Counselling to Survivors of Sexual Violence: A Scoping Review of Promising and Best Practices
Bibliographic record
Abstract
Distance counselling holds immense potential for improving access to trauma supports for survivors of sexual violence (SV), and particularly for under-served groups who disproportionately experience violence and myriad barriers to accessing in-person supports. And yet, the evidence-base for the practice and delivery of distance counselling remains under-developed. In the context of COVID-19, where telehealth applications have undergone a rapid uptake, we undertook a scoping review of existing evidence of therapeutic and organizational practices related to the real-time (synchronous) delivery of distance counselling to survivors of SV. We based our scoping review methods on Arksey and O'Malley framework and in accordance with the guidance on scoping reviews from the Joanna Briggs Institute (JBI) and PRISMA reporting guidelines for scoping reviews. A comprehensive search of MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Sociological Abstracts was undertaken in October 2020, and again in March 2022. Searching, reviewing, appraisal, and data extraction was undertaken by two reviewers. In total, 1094 records were identified that resulted in 20 studies included. Descriptions, findings, and recommendations were gleaned and synthesized into potential practices using inductive thematic analysis. While many studies have an appreciative orientation to distance counselling, these benefits tend to be framed as non-universal, and conditional on survivor safety, flexibility, anonymity, survivor choice, strong and inclusive technology, and a supported workforce.Despite the limited evidence-base, we present several clusters of findings that, taken together, can be used to support current COVID-19 distance counselling initiatives with survivors, as well as guide the future development of best practices.
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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 | 0.047 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".