Sexual Satisfaction in Transgender Survivors of Childhood Sexual Abuse
Bibliographic record
Abstract
Previous studies that have considered the impact of childhood sexual abuse (CSA) on adult sexuality have neglected the experiences of transgender survivors of CSA. Specifically, the existing published literature on adult sexual functioning and satisfaction tends to focus nearly exclusively on the experiences of cisgender, heterosexual individuals despite a high rate of CSA victimization among the transgender community. To address the gaps in the research, this study seeks to understand the impact of childhood sexual abuse on adult sexual satisfaction in two-spirit, transgender, and non-binary (2STNB) survivors. A sample of 317 2STNB individuals from the community were recruited through social media posts and community organizations across Canada to complete an online survey on Qualtrics. Data was analyzed using a factorial ANOVA and a PROCESS macro. Results indicated that sexual satisfaction scores did not differ regardless of CSA history for 2STNB individuals, and these scores did not differ based on the gender identity (i.e., transgender man, transgender women, non-binary individuals) of the survivor. Results from the PROCESS macro suggested that the severity of abuse did not impact sexual satisfaction scores, and this relationship was not mediated by post-traumatic stress symptoms or intimate partner violence. These results were aligned with some previous research findings but challenged others. Results from this study may help inform clinical interventions, 2STNB-inclusive research, and help survivors and their loved ones understand how a CSA history may impact their sexual satisfaction.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".