Scholarlink

<p>Minimal clinically important difference (MCID) is a clinical change metric that can offset setbacks in statistical and effect size approaches. MCIDs may be especially important for characterizing change mechanisms to understand intervention impact. However, MCIDs are rarely examined within the context of mental health-based interventions. The current study employed anchor- and distribution-based methods to derive MCIDs for hopelessness (a change mechanism) within a dataset for publicly accessible, open access interventions. Derived MCIDs were then examined within a randomized controlled trial dataset to understand their relation to prospective depressive symptoms. A cohort of 917 youth aged 11-17 years old (64.4% female, White: 30.3%) participated in brief, digital, self-guided interventions targeting depressive symptoms within the open access dataset. A separate cohort of 1,282 youth aged 13-16 years old (87.9% female, White: 66.7%) engaged with the same interventions in the randomized controlled trial dataset. MCIDs ranged from 0.13 to 0.38&#xa0;units of change on a 4-point hopelessness scale. After correcting for multiple hypotheses, the MCIDs derived via the publicly accessible, open access interventions dataset did not translate to significant (q &lt; 0.05) differences in three-month post-intervention depressive symptom scores within the randomized controlled trial dataset. Findings from this study led to important considerations for leveraging MCIDs within mental health interventions and outline a potential empirical agenda to further characterize the utility of this change metric.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of the Minimal Clinically Important Difference to Adolescent Mental Health Interventions: A Case Study for Hopelessness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

查看全文
  • Hena Thakur,
  • John T. Parkhurst,
  • Jessica L. Schleider,
  • Damien Lekkas

摘要

Minimal clinically important difference (MCID) is a clinical change metric that can offset setbacks in statistical and effect size approaches. MCIDs may be especially important for characterizing change mechanisms to understand intervention impact. However, MCIDs are rarely examined within the context of mental health-based interventions. The current study employed anchor- and distribution-based methods to derive MCIDs for hopelessness (a change mechanism) within a dataset for publicly accessible, open access interventions. Derived MCIDs were then examined within a randomized controlled trial dataset to understand their relation to prospective depressive symptoms. A cohort of 917 youth aged 11-17 years old (64.4% female, White: 30.3%) participated in brief, digital, self-guided interventions targeting depressive symptoms within the open access dataset. A separate cohort of 1,282 youth aged 13-16 years old (87.9% female, White: 66.7%) engaged with the same interventions in the randomized controlled trial dataset. MCIDs ranged from 0.13 to 0.38 units of change on a 4-point hopelessness scale. After correcting for multiple hypotheses, the MCIDs derived via the publicly accessible, open access interventions dataset did not translate to significant (q < 0.05) differences in three-month post-intervention depressive symptom scores within the randomized controlled trial dataset. Findings from this study led to important considerations for leveraging MCIDs within mental health interventions and outline a potential empirical agenda to further characterize the utility of this change metric.