Using Technology to Conduct Focus Groups With a Hard-to-Reach Population: A Methodological Approach Concerning Male Victims of Partner Abuse in Four English-Speaking Countries
Bibliographic record
Abstract
Research shows that the experiences of male victims of partner abuse (PA) are often denied by the public and the professionals who are charged to support PA victims. Recruiting female victims for research on PA victimization is relatively easy because there are existing structures to serve this group of victims. Thus, male victims are considered a hard-to-reach (HTR) population, and studying them can be difficult. This article focuses on the use of technology to collect qualitative data from male PA victims in an international study focusing on male victims. The researchers used their own professional networks to recruit and screen a convenience sample of male victims of female-to-male PA, in four different English-speaking countries: Australia, Canada, England, and the United States. Four web-based, video-enabled, focus groups were held for each country-for a total of 12 groups and 41 male participants. This article addresses recruitment methods, the use of technology in data collection, protecting the confidentiality of male victims, methods for informed consent, and lessons learned to facilitate future research.
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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 | 0.086 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".