The Experience of the Coercive Control Scale: Factor Structure and Psychometric Properties
Bibliographic record
Abstract
Background: Coercive control is an important topic related to couples’ relationships, and, therefore, appropriate measures are needed to assess this factor. Coercive control has three facets: (1) the abuser’s intentionality or goal orientation vs. motivation, (2) negative perceptions of controlling behaviors by the victim, and (3) the abuser’s ability to gain control through credible threats. Objectives: This study aimed to devise a valid and reliable measure of coercive control in Iran. Methods: A coercive control scale based on the Canadian Violence Against Women Survey and Psychological Maltreatment of Women Survey was translated and back-translated. Based on the experts’ opinions, some items were added to the questionnaire, while others were changed to fully capture the nature of coercive control in Iran. The scale was named the Experiences of Coercive Control (ECC) Scale. The study period was between May and August 2021. Results: The test-retest reliability of the ECC Scale was high, and the convergent validity of this scale with the Wife Abuse Questionnaire was confirmed. The analysis of the factor structure of the ECC Scale based on the principal component analysis method with a varimax rotation yielded a two-factor solution, including control via aggression and spying behaviors. Conclusions: The ECC Scale is a valid and reliable measure that could be used in emergency and non-emergency situations. The need to include more culture-appropriate items should be discussed in future research.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".