S106. MONITORING SOCIAL FUNCTIONING THROUGH A MOBILE-BASED APPLICATION FOR YOUTH AT-RISK OF PSYCHOSIS: 2-MONTH FEASIBILITY STUDY
Bibliographic record
Abstract
Abstract Background Youth at clinical high-risk (CHR) for developing psychosis are characterized by long-standing social deficits and isolation compared to healthy youth. Because poor social functioning is predictive of transition to psychosis, it is important to monitor its fluctuations. Objectives: 1) To test initial usability of an app (SOMO); and 2) to confirm that SOMO is acceptable, feasible, and safe to monitor daily social functioning among youth at clinical high-risk (CHR) for developing psychosis. Methods Participants: 24 CHR participants (12–30 years old) used SOMO for 2 months to test its initial feasibility to monitor social functioning. Measures: 1) SOMO comprises 13 daily questions regarding social interactions in-person or online covering: type of relationship, time spent together, quality of the interaction, activities done, conflict and resolution, meaningfulness of the interaction, subjective opinion of the socialization, and level of loneliness. 2) Social functioning was assessed with the Social Functioning Scale (GF:S), which assesses peer relationships, peer conflict, age-appropriate intimate relationships, and involvement with family members. 3) Qualitative data of the SOMO was gathered through the 23-item Mobile Application Rating Scale (MARS) covering questions about engagement, functionality, aesthetics, information provided, and subjective quality of SOMO. Analyses. a) Descriptive information of 1) usability data (i.e., loggings, social relationships, ad meaningfulness) and 2) the app quality ratings (i.e., engagement, functionality, aesthetics, and information) was collected. Results There were 750 loggings over the 2-month testing period, with 50% of participants logging in at least every other day. Participants had 690 in-person interactions and 497 online interactions. The most meaningful interactions were considered the ones with their partner, followed by interactions with friends, casual friends, family, others and strangers in-person respectively. Participants reported conflict in 18.2% of their interactions. SOMO obtained a high overall score on the MARS (M=4.38). Ratings for engagement (M=3.91), functionality (M=4.54), aesthetics (M=4.56), information (M=4.51), subjective score (M=3.89), and perceived impact in behavior (M=3.52) were higher than other relevant mHealth apps. All participants rated SOMO as safe. Social functioning did not change significantly after using SOMO. Discussion SOMO demonstrated initial acceptability, feasibility, and safety among CHR participants.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".