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Record W4403099672 · doi:10.1080/00224499.2024.2403024

Delphi Recommendations for Diagnosis and Treatment of Sexual Interest in Children in Non-Mandated Community Settings

2024· article· en· W4403099672 on OpenAlexaff
Skye Stephens, Sara Jahnke, Myles Davidson

Bibliographic record

VenueThe Journal of Sex Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsDelphi methodDelphiMedical educationPsychologyMedicineFamily medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study sought to reach an international expert consensus regarding the diagnosis and treatment of adults with a sexual interest in children in cases where treatment is non-mandated. An expert panel of 27 participants (19 professionals and eight individuals with sexual interest in children) completed at least one of three rounds of an online Delphi survey. In the first round, participants responded to open-ended questions about assessment approaches, treatment goals, treatment approaches, and adapting assessment and treatment for sexual exclusivity. In rounds two and three, participants rated their level of agreement with each of the recommendations derived from Round 1. Consensus was reached for 48 practice guidelines, particularly for recommendations that relate to general clinical practice, such as assessing for other mental health conditions. There was more contention for the inclusion of forensic practices. The present recommendations can serve as a set of tentative guidelines that may guide assessment and treatment of sexual interest in children in non-mandated settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.396
metaresearch head score (Gemma)0.361
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.396
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3960.361
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0090.005
Science and technology studies0.0080.008
Scholarly communication0.0050.007
Open science0.0070.016
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0090.004

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.

Opus teacher head0.443
GPT teacher head0.567
Teacher spread0.123 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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