The FAIIR conversational AI agent assistant for youth mental health service provision
Bibliographic record
Abstract
Frontline crisis support plays a critical role in youth mental health services, where Crisis Responders (CRs) engage in conversations and assign issue tags to guide interventions. To enhance this process, we introduce FAIIR (Frontline Assistant: Issue Identification and Recommendation), an ensemble of domain-adapted transformer models trained on 780,000 conversations. FAIIR aims to reduce CR's cognitive burden, enhance issue identification accuracy, and streamline post-conversation administrative tasks. Evaluated on retrospective data, FAIIR achieves an average AUC ROC of 94%, an average F1-score of 64%, and an average recall score of 81%. During the silent testing phase, its performance remained robust, with less than a 2% drop in all metrics. CRs exhibited 90.9% agreement with its predictions, and expert agreement with FAIIR exceeded their agreement with original labels. These findings highlight FAIIR's potential to assist CRs in prioritizing urgent cases and ensuring appropriate resource allocation in crisis interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".