Core competencies for BESTCO certified sex therapists
Bibliographic record
Abstract
In 2015, the leadership of the Board of Examiners in Sex Therapy and Counselling of Ontario (BESTCO) struck a committee to establish the first set of core competencies for the field of sex therapy. The inter-disciplinary committee used a modified Delphi approach. The resulting document, introduced here, was ratified by the BESTCO membership in 2019. BESTCO members must be able to demonstrate competence in assessment and therapy in core clinical areas as a condition of certification. Eleven clinical topics were identified for assessment and therapy with general and key features for each topic. These clinical topics include not only the DSM disorders (e.g., arousal difficulties, orgasm difficulties) but also broader areas including Lack of Knowledge about Sexuality, Desire Discrepancies, and Sexual Sequelae of Sexual Assault, Abuse or Other Trauma. The General Key Features for assessment are organized into 7 domains: History of the Problem, Sexual History, Medical/Psychiatric, Psychological, Cultural, Relational and Quality of Sex. The General Key Features for Therapy are organized in terms of principles of Clinical Orientation, Clinical Formulation, Referral/Collaboration, Psychoeducation, Interventions and Therapy. The emphasis in this document is on allowing therapists of differing clinical perspectives to articulate principles of assessment and case conceptualization from within their own frames of reference. We affirm that this must be a fluid document, updated to reflect developments in language, assessment, conceptualization, and treatment as those factors shift.
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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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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.
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".