Objective:

First-generation community college students face unique risks for mental health distress, which can place them at risk for attrition and a myriad of other negative consequences. The aim of the present quantitative investigation was to test the utility of the REDFLAGS model, a mental health literacy based tool for supporting mental wellness, with a national sample of first-generation community college students.

Method:

Confirmatory factor analysis (CFA), logistic regression analysis, and a factorial analysis of variance (ANOVA) were computed to test the utility of the REDFLAGS model as a tool for promoting first-generation community college students’ mental health.

Results:

The CFA demonstrated that the dimensionality of the REDFLAGS model was estimated adequately with first-generation community college students. First-generation community college students’ recognition of the REDFLAGS as warning signs for mental distress emerged as a significant positive predictor of making a peer-to-peer referral to the counseling center. The factorial ANOVA revealed that first-generation community college students who were members of a Greek Organization were more likely to identify the REDFLAGS as warning signs for mental distress.

Contributions:

Previous investigators established multiple strategies for supporting the mental health needs of either first-generation or community college students. First-generation community college student mental health, however, has received little attention. This study demonstrates the utility of the REDFLAGS model with first-generation community college students. Considering the dearth of literature on first-generation community college student mental health, the REDFLAGS model offers novel implications for promoting the mental health needs of first-generation students enrolled in community colleges.

中文翻译：

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支持第一代社区大学生心理健康的心理健康素养方法：REDFLAGS模型

客观的：

第一代社区大学生面临着心理健康困扰的独特风险，这可能使他们面临流失的风险以及许多其他负面后果。当前定量研究的目的是使用第一代社区大学生的全国样本，测试REDFLAGS模型的实用性，该模型是一种基于心理健康素养的支持心理健康的工具。

方法：

计算验证性因素分析（CFA），逻辑回归分析和方差因子分析（ANOVA），以测试REDFLAGS模型作为促进第一代社区大学生心理健康的工具的效用。

结果：

CFA证明，第一代社区大学生对REDFLAGS模型的维度进行了适当的估计。第一代社区大学生对REDFLAGS的认可是心理困扰的警告信号，它成为将对等推荐给咨询中心的一项重要的积极预测指标。阶乘方差分析显示，属于希腊组织成员的第一代社区大学生更有可能将REDFLAGS识别为精神困扰的警告信号。

贡献：

以前的研究人员建立了多种策略来支持第一代或社区大学生的心理健康需求。但是，第一代社区大学生的心理健康却很少受到关注。这项研究证明了REDFLAGS模型在第一代社区大学生中的作用。考虑到缺乏关于第一代社区大学生心理健康的文献，REDFLAGS模型为促进社区大学就读的第一代学生的心理健康需求提供了新的含义。