Find Supports for the Post about Mental Issues: More Than Semantic Matching
Why this work is in the frame
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Bibliographic record
Abstract
Mental-health-oriented question-answering (MH-QA) aims at retrieving an appropriate response for a question post involving mental health issues, which will be used to assist counsellors to reply to the support seeker. This task is different from the general QA task because many additional criteria such as emotions are involved. In this paper, we propose the Multi-Feature Graph Convolutional Network model (MF-GCN) to integrate not only semantic features, but also mental health related features and context features, to match question posts and responses. Different types of feature are exploited through multiple graph convolutional networks. A new dataset is constructed for MH-QA to evaluate our model. Experimental results show that our model with mental health features significantly outperforms a wide range of state-of-the-art models without them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it