Recruiting Domestically Violent Fathers and Families for Research: What Does It Take?
Bibliographic record
Abstract
Empirical literature on strategies to effectively recruit participants for research is sparse, especially within the domestic violence domain. Evaluating recruitment methodology alongside researcher effort, time, and costs provides realistic guidelines for research planning. This study examined recruitment of fathers with and without a history of domestic violence perpetration into longitudinal research. Data were collected on 196 fathers we attempted to recruit for Time 1 assessment and 151 fathers we attempted to retain for Time 2 assessment over an eight-month timeframe. Results indicated that domestically violent fathers required similar efforts to recruit initially but required more effort for follow-up and that recruitment for father-child and mother assessments with this group was particularly challenging. Tests of two specific recruitment strategies demonstrate advantages of in-person and immediate scheduling of research appointments. Descriptive information is provided on the time and resources required for recruiting high-risk fathers into research and recommendations for conducting future research with this population are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".