Bibliographic record
Abstract
Three of the major trends in the field of sex therapy are reviewed. The first of these is the prevailing conception of sex therapy as the treatment of symptoms of sexual dysfunctions and disorders. The weak early, theoretical foundations’ of sex therapy permitted the confounding of symptoms of sexual difficulties with underlying problems per se. This confusion has eventuated in the under-development of the field, now apparent in theory, research, practice and training. The second trend is the continuing and accelerating medicalization of sexuality, sexual problems and their treatment. Impressive advances in biomechanical and pharmacological methods are being embraced whether or not they apply to a science of human sexuality. Concurrently, market-driven obstacles to the growth of the field (e.g., the reluctance of HMOs to reimburse couples for treatment of relational problems) act as deterrents to studying and providing comprehensive treatment options. The third trend is the fragmentation of our field, such that both the nature of services provided and the profession itself are becoming increasingly splintered. The need for an interdisciplinary meeting ground for clinicians of diverse backgrounds is highlighted. These trends and their implications for sex therapy are explored.
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 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".