Using the 3-factor Sexual Desire Inventory to understand sexual desire in a sexually diverse sample with and without sexual interest/arousal disorder
Bibliographic record
Abstract
The current literature on sexual desire is often limited to the experiences of heterosexual cisgender individuals. Individuals who identify as lesbian, gay, bisexual, transgender, queer (or sometimes questioning) (LGBTQ+) may experience sexual desire and relationship configurations differently than their heterosexual counterparts. The purpose of the study was to use the 3-factor structure of the Sexual Desire Inventory to compare LGBTQ+ and heterosexual cisgender individuals with and without sexual interest/arousal disorder (SIAD). The three domains are dyadic sexual desire towards partner, dyadic sexual desire for attractive other, and solitary sexual desire. A sample of 98 LGBTQ+ individuals and 65 heterosexual cisgender individuals ( Mage = 31.2, SD = 9.1) were a part of a larger ongoing study where they completed online measures of demographics and sexual desire. We carried out 2x2 ANOVAs to compare desire domains among four subsamples: LGBTQ+ without SIAD, LGBTQ+ with SIAD, cisgender heterosexual without SIAD, and cisgender heterosexual with SIAD. There was a main effect of SIAD status on dyadic desire for a partner and for an attractive other such that those with SIAD had lower desire. There was a main effect of SIAD status and group for solitary sexual desire, such that those without SIAD; LGBTQ+ individuals reported significantly higher solitary desire, which could be explained by higher sexual positivity in this population. Future studies should explore the impact of relationship structures on these separate domains of dyadic desire in sexually diverse groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".