The impact of intimate partner violence on facial emotion recognition among Korean baby boomers
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) can have lasting psychological and cognitive effects, potentially impairing facial emotion recognition (FER). This study examines the accuracy of FER among IPV survivors compared to individuals without IPV experience within the Korean baby boomer generation, aged 60-69, exploring the relationship between IPV, post-traumatic stress disorder (PTSD) symptoms, and FER abilities.Objective: To assess whether IPV impacts FER accuracy and intensity and to investigate whether symptoms of PTSD moderate this relationship.Method: The study included 80 participants, with 31 % identified as IPV survivors. A self-administered survey collected information on lifetime experiences of physical, emotional, and sexual abuse, as well as assessments for PTSD symptoms. Participants completed the Korean Montreal Cognitive Assessment (K-MOCA) and performed 70 FER tasks to evaluate accuracy and intensity of facial emotions. Logistic regressions were used to analyse the relationship between IPV, PTSD symptoms, and FER performance.Results: IPV survivors demonstrated 0.64 times lower accuracy in recognizing overall facial emotions, including anger, sadness, surprise, and neutral expressions Additionally, IPV survivors exhibited significantly lower intensity scores for overall facial expressions. Significant interaction terms between IPV and PTSD symptoms indicate that PTSD symptoms moderate the effect of IPV on the FER, as well as neutral and sad facial expressions.Conclusions: IPV can disrupt one's ability to recognize facial emotions, and PTSD symptoms may moderate this impairment. This highlights the potential benefits of assisting IPV survivors with emotion recognition as part of their recovery process, which could enhance both social connections and their safety.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".