Feasibility of an Automated System Counselor for Survivors of Sexual Assault
Bibliographic record
Abstract
Background: Sexual assault (SA) is common and costly to individuals and society, and increases risk of mental health disorders. Stigma and cost of care discourage survivors from seeking help. Norms profiling survivors as heterosexual, cisgendered women dissuade LGBTQIA+ individuals and men from accessing care. Because individuals prefer disclosing sensitive information online rather than in-person, online systems—like instant messaging and chatbots—for counseling may bypass concerns about stigma. These systems’ anonymity may increase disclosure and decrease impression management, the process by which individuals attempt to influence others’ perceptions. Their low cost may expand reach of care. There are no known evidence-based chat platforms for SA survivors. Objective: To examine feasibility of a chat platform with peer and automated system (chatbot) counseling interfaces to provide cognitive reappraisals (a cognitive behavioral therapy technique) to survivors. Methods: Participants are English-speaking, US-based survivors, 18+ years old. Participants are told they will be randomized to chat with a peer or automated system counselor 5 times over 2 weeks. In reality, all participants chat with a peer counselor. Chats employ a modified-for-context evidence-based cognitive reappraisal script developed by Koko, a company offering support services for emotional distress via social networks. At baseline, participants indicate counselor type preference and complete a basic demographic form, the Brief Fear of Negative Evaluation Scale, and self-disclosure items from the International Personality Item Pool. After 5 chats, participants complete questions from the Client Satisfaction Questionnaire (CSQ), Self-Reported Attitudes Toward Agent, and the Working Alliance Inventory. Hypotheses: 1) Online chatting and automated systems will be acceptable and feasible means of delivering cognitive reappraisals to survivors. 2) High impression management (IM≥25) and low self-disclosure (SD≤45) will be associated with preference for an automated system. 3) IM and SD will separately moderate the relationship between counselor assignment and participant satisfaction. Results: Ten participants have completed the study. Recruitment is ongoing. We will enroll 50+ participants by 10/2017 and outline findings at the Connected Health Conference. To date, 70% of participants completed all chats within 24 hours of enrollment, and 60% indicated a pre-chat preference for an automated system, suggesting acceptability of the concept. The post-chat CSQ mean total score of 3.98 on a 5-point Likert scale (1=Poor; 5=Excellent) suggests platform acceptability. Of the 50% reporting high IM, 60% indicated preference for an automated system. Of the 30% reporting low SD, 33% reported preference for an automated system. At recruitment completion, ANOVA analyses will elucidate relationships between IM, SD, and counselor assignment. Correlation and linear regression analyses will show any moderating effect of IM and SD on the relationship between counselor assignment and participant satisfaction. Conclusions: Preliminary results suggest acceptability and feasibility of cognitive reappraisals via chat for survivors, and of the automated system counselor concept. Final results will explore relationships between SD, IM, counselor type, and participant satisfaction to inform the development of new platforms for survivors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".