The Growing Regulation of Conversion Therapy
Bibliographic record
Abstract
Conversion therapies are any treatments, including individual talk therapy, behavioral (e.g. aversive stimuli), group therapy or milieu (e.g. "retreats or inpatient treatments" relying on all of the above methods) treatments, which attempt to change an individual's sexual orientation from homosexual to heterosexual. However these practices have been repudiated by major mental health organizations because of increasing evidence that they are ineffective and may cause harm to patients and their families who fail to change. At present, California, New Jersey, Oregon, Illinois, Washington, DC, and the Canadian Province of Ontario have passed legislation banning conversion therapy for minors and an increasing number of US States are considering similar bans. In April 2015, the Obama administration also called for a ban on conversion therapies for minors. The growing trend toward banning conversion therapies creates challenges for licensing boards and ethics committees, most of which are unfamiliar with the issues raised by complaints against conversion therapists. This paper reviews the history of conversion therapy practices as well as clinical, ethical and research issues they raise. With this information, state licensing boards, ethics committees and other regulatory bodies will be better able to adjudicate complaints from members of the public who have been exposed to conversion therapies.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".