Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper proposed an ensemble-based method to classify mental illnesses. The proposed method achieves similar performance with baseline models (measured in accuracy and F1) but has better uncertainty (measured in ECE, MCE and Brier).
strengths_and_weaknesses: Strengths:
1. The paper explains the motivation, what existing methods lack, and their proposed method well.
2. The problem itself seems to be of interest for people working in mental health industry.
3. The result, despite tested only on one dataset (Murarka et al. 2021), seems promising.

Weaknesses:
1. The contribution of this work might be very limited to this specific application (or even this dataset). I am not convinced that the proposed method would be of interest to broader audiences outside of mental health.
2. Experiment could include other datasets as well, such as the ones used in Benton et al. (2017).
suggested_changes: 1. Experiment with more datasets for mental illnesses.
2. Although the target application is to classify mental health, such high uncertainty issue is not only applied to this specific fields. Does the proposed method can benefit other applications as well?
review_title: null

Meta-review: metareview: The paper explains the motivation, existing works, and the proposed architecture well. The method may be interesting in the specific domain and the results look promising. However, the framing and the scope are limited to a specific and narrow application only (mental health), which limits the applicability and potential impact of the paper. 

confidence: 3: The area chair is somewhat confident
Decision: Invite to archive