RELUCTANCE IN TREATING MINOR ATTRACTED PERSONS: A SURVEY ANALYSIS OF SEX THERAPISTS’ CONCERNS AND BARRIERS
Bibliographic record
Abstract
Abstract Objectives Reluctance among sex therapists to work with Minor Attracted Persons (MAPs) presents a barrier to providing comprehensive, nonjudgmental care. This study aims to identify the primary factors behind this reluctance among trained sex therapists to inform future enhancements in educational resources and professional guidelines. Methods A survey was administered to 34 licensed clinicians specializing in sex therapy, recruited through the members only Listserv of the largest certifying organization for sex therapists in the United States, which certifies professionals in the U.S., Canada, Mexico, and Israel. All participants had completed formal training in the field and participated in Sexual Attitudes Reassessment (SAR) workshops. Respondents were asked to report their reasons for declining to work with MAP clients. Results The survey found that 40% of therapists cited personal bias or discomfort as their primary reason for refusing MAP clients, reflecting a notable level of stigma even within professionally trained practitioners. Additionally, 27.5% reported uncertainty regarding legal obligations as a key concern, closely followed by 25% who cited a lack of specific training on MAP-related issues. These findings suggest that even extensive training may not fully overcome personal biases, nor provide sufficient legal or specialized training guidance. Conclusions The reluctance to work with MAP clients among trained sex therapists could be addressed by integrating focused training on personal biases, clarifying legal responsibilities, and enhancing MAP-specific therapeutic competencies. This research highlights the need for improved frameworks within sex therapy education and legal guidance to support the development of inclusive, professionally competent therapeutic practices. Conflicts of Interest There are no conflicts of interest.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".