Psychological stress detection is an important task for mental healthcare research, but there has been little prior work investigating the effectiveness of psychological stress models on minority individuals, who are especially vulnerable to poor mental health outcomes. In this work, we use the related task of minority stress detection to evaluate the ability of psychological stress models to understand the language of sexual and gender minorities. We find that traditional psychological stress models underperform on minority stress detection, and we propose using emotion-infused models to reduce that performance disparity. We further demonstrate that multi-task psychological stress models outperform the current state-of-the-art for minority stress detection without directly training on minority stress data. We provide explanatory analysis showing that minority communities have different distributions of emotions than the general population and that emotion-infused models improve the performance of stress models on underrepresented groups because of their effectiveness in low-data environments, and we propose that integrating emotions may benefit underrepresented groups in other mental health detection tasks.

翻译：心理压力检测是心理健康研究的重要任务，但此前很少有研究探讨心理压力模型对少数群体的有效性——这类群体尤其容易面临不良心理健康结果。本研究利用少数群体压力检测这一相关任务，评估心理压力模型理解性少数与性别少数群体语言的能力。我们发现传统心理压力模型在少数群体压力检测中表现欠佳，并提出使用情绪增强模型来缩小这一性能差距。我们进一步证明，多任务心理压力模型在不直接使用少数群体压力数据进行训练的情况下，仍能超越当前最先进的少数群体压力检测方法。通过解释性分析，我们揭示了少数群体与普通人群在情绪分布上的差异性，并表明情绪增强模型通过其在低数据环境中的有效性，提升了压力模型对代表性不足群体的检测性能。我们建议，在心理健康检测的其他任务中，整合情绪因素可能同样有助于改善对代表性不足群体的检测效果。