Development and validation of the MIDSA-SC scale
Bibliographic record
Abstract
In recent years, a lot of interest has been devoted to the study of sexual coercion. Although the definition varies across studies, sexual coercion is generally defined as the employment of tactics to obtain sexual activity against freely given consent. Several measures of sexual coercion exist, however those measures present some methodological limitations. The purpose of this study was to create and validate a scale of sexual coercion using items from the Multidimensional Inventory of Development, Sex, and Aggression and assess its validity. The sample included 529 adult males who were incarcerated for a sexual offence. Results revealed that a 5-item version of the sexual coercion scale had the best psychometric properties, with good internal consistency, convergent and concurrent validity. Furthermore, the item response theory analysis shows that most items were considered difficult and that all items discriminate between individuals at different levels along the continuum.PRACTICE IMPACT STATEMENT The construction and validation of a sexual coercion scale would also allow for more appropriate programmes and interventions. A better understanding of the continuum (i.e. to understand the continuum as a continuum of tactics) would help educate individuals about inappropriate sexual behaviours and the concept of consent. To do so, professionals require appropriate instruments to accurately measure this complex problem.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".