Measuring Technology-Facilitated Sexual Violence and Abuse in the Chinese Context: Development Study and Content Validity Analysis
Bibliographic record
Abstract
BACKGROUND: Technology-facilitated sexual violence and abuse (TFSVA) encompasses a range of behaviors where digital technologies are used to enable both virtual and in-person sexual violence. Given that TFSVA is an emerging and continually evolving form of sexual abuse, it has been challenging to establish a universally accepted definition or to develop standardized measures for its assessment. OBJECTIVE: This study aimed to address the significant gap in research on TFSVA within the Chinese context. Specifically, it sought to develop a TFSVA measurement tool with robust content validity, tailored for use in subsequent epidemiological studies within the Chinese context. METHODS: The first step in developing the measurement approach for TFSVA victimization and perpetration was to conduct a thorough literature review of existing empirical research on TFSVA and relevant measurement tools. After the initial generation of items, all the items were reviewed by an expert panel to assess the face validity. The measurement items were further reviewed by potential research participants, who were recruited through snowball sampling via online platforms. The assessment results were quantified by computing the content validity index (CVI). The participants were asked to rate each scale item in terms of its relevance, appropriateness, and clarity regarding the topic. RESULTS: The questionnaire was reviewed by 24 lay experts, with a mean age of 27.96 years. They represented different genders and sexual orientations. The final questionnaire contained a total of 89 items. Three key domains were identified to construct the questionnaire, which included image-based sexual abuse, nonimage-based TFSVA, and online-initiated physical sexual violence. The overall scale CVI values of relevance, appropriateness, and clarity for the scale were 0.90, 0.96, and 0.97, respectively, which indicated high content validity for all the instrument items. To ensure the measurement accurately reflects the experiences of diverse demographic groups, the content validity was further analyzed by gender and sexual orientation. This analysis revealed variations in item validity among participants from different genders and sexual orientations. For instance, heterosexual male respondents showed a particularly low CVI for relevance of 0.20 in the items related to nudity, including "male's chest/nipples are visible" and "the person is sexually suggestive." This underscored the importance of an inclusive approach when developing a measurement for TFSVA. CONCLUSIONS: This study greatly advances the assessment of TFSVA by examining the content validity of our newly developed measurement. The findings revealed that our measurement tool demonstrated adequate content validity, thereby providing a strong foundation for assessing TFSVA within the Chinese context. Implementing this tool is anticipated to enhance our understanding of TFSVA and aid in the development of effective interventions to combat this form of abuse.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".