A Latent Class Analysis of Reproductive Coercion Experiences Based on Victim-Survivors’ Acknowledgment and Disclosure Patterns
Bibliographic record
Abstract
Reproductive coercion (RC) is a form of violence involving behavior that interferes with an individual's contraceptive and reproductive decisions. Like other forms of violence perpetrated by intimate partners, victims of RC do not necessarily identify it as such. Similarly, victim-survivors do not readily disclose their experiences or seek support and treatment. This study identifies patterns of acknowledgment and formal and informal disclosure of RC experiences in a community sample of 317 participants. Latent classes are then compared with respect to characteristics of victims/survivors, RC consequences, and associated contexts. Participants completed measures to assess experiences of RC and violence perpetuated by intimate partners as well as social support, posttraumatic stress symptoms, and consequences for psychological and sexual health. Latent class analysis was performed to identify acknowledgment and disclosure patterns. An optimal three-class solution was selected: High unacknowledgment with ambivalence, High disclosure (41%); High acknowledgment, High disclosure (30%); and Hesitant acknowledgment, No disclosure (29%). Classes were identified according to the presence of social support, living with a disability, victimization experiences, and mental and sexual health consequences. Future studies should explore the relationship between RC acknowledgment and disclosure, which can influence victims' search trajectories for support and services.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".