Recruitment Issues in Research with People Who are Attracted to Children: A Systematic Review
Bibliographic record
Abstract
There has been an increase in research using online forums for individuals who are attracted to children. This research is beneficial because it allows the study of individuals attracted to children recruited from the community, in contrast to individuals recruited from clinical or forensic samples. The aim of the present review was to explore who researchers are recruiting from online forums and how they are recruiting these participants. We conducted a systematic review of 71 studies with participants recruited from online forums for people attracted to children. We considered sample homogeneity, evidence of data and sample overlap, and the use of community-based research principles that aid in participant recruitment and retention. The majority of participants were White men, between the ages of 18 and 35, living in North America or Europe. Forty percent of publications had some data overlap. Virtuous Pedophiles and B4U-ACT were the two most common forums for recruitment. Just over one-third of publications mentioned community-based research principles. Our results suggest a high degree of homogeneity across online samples of individuals attracted to children, with most participants being recruited from two forums. Results from publications recruiting narrowly are less generalizable and may give an inaccurate impression of replication. Conducting research with participants recruited from the community is important, but researchers should diversify recruitment methods, ask about previous study participation, and employ community-based research principles to increase participation.
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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 | 0.095 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".