Predicting mental health help seeking orientations among diverse Undergraduates: An ordinal logistic regression analysis✰

Stewart, Genéa; Kamata, Akihito; Miles, Rona; Grandoit, Evan; Mandelbaum, Faigy; Quinn, Crystal

doi:10.1016/j.jad.2019.07.058

Cited by 27 publications

(18 citation statements)

References 40 publications

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“…Although in general chatbots show promising results (Provoost et al, 2017;Lattie et al, 2019), they are focused on offering therapy, and individuals may not use a health care service due to fears of stigma (Clement et al, 2015;Stewart et al, 2019). To illustrate: fewer than half of the college students who report suffering from one or more mental disorders seek treatment for those problems (Zivin et al, 2009;Auerbach et al, 2018;Stewart et al, 2019). Furthermore, the majority of students will probably not require cognitive behavioral therapy but would benefit from individualized coaching to overcome the problems they face during the transition to tertiary education.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting

et al. 2020

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“…The ordinal regression approach was chosen because it is more appropriate for data measured on a Likert scale compared to common statistical models which may simplify the collected data by assuming equal intervals between the scoring categories. Ordinal regression analysis, based on the cumulative-odds principle, treats the dependent variables as an ordered categorical variable (Stewart, et al, 2019;Gutierrez, et al, 2016;Osborne, 2015;Hosmer, et al, 2013). The results of ordinal regression can be easily interpreted and are straightforward regarding the occurrence probability of an event.…”

Section: Applied Methodsmentioning

confidence: 99%

Social influence and impact of social media on users’ mobility decisions

Karatsoli

Nathanail

2021

J. sustain. dev. transp. logist.

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“…Differences across quartiles were assessed by one-way analysis of variance for continuous variables and the chi-square test for categorical variables. The association between each physical activity and degree of happiness or ikigai was assessed using an ordinal logistic regression model (McCullagh, 1980;Stewart et al, 2019). This model is similar to conventional logistic regression approaches except that an ordered categorical variable is treated as a dependent variable.…”

Section: Unemployedmentioning

confidence: 99%

Associations of physical activity in rural life with happiness and ikigai: a cross-sectional study

Kabasawa

Tanaka

Ito

et al. 2021

Humanit Soc Sci Commun

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Physical activity is associated with subjective well-being. In rural communities, however, physical activity may be affected by environmental factors (e.g., nature and socioecological factors). We examined the association of two physical activities in rural life (farming activity and snow removal) with subjective well-being in terms of happiness and ikigai (a Japanese word meaning purpose in life). In this cross-sectional study, we analysed data collected from community-dwelling adults aged ≥ 40 years in the 2012–2014 survey of the Uonuma cohort study, Niigata, Japan. Happiness (n = 31,848) and ikigai (n = 31,785) were evaluated with respect to farming activity from May through November and snow removal from December through April by using an ordinal logistic regression model with adjustments for potential confounders. The analyses were conducted in 2019. Among the participants who reported some farming or snow-removal time, median farming and snow-removal time (minutes per day) was 90.0 and 64.3 for men and 85.7 and 51.4 for women, respectively. Ordinal logistic regression analysis showed that longer time farming was associated with greater happiness and ikigai in men (adjusted odds ratio for first vs. fourth quartile: happiness = 1.17, 95% confidence interval [CI] = 1.01, 1.35; ikigai = 1.29, 95% CI = 1.10, 1.50), and also in women (adjusted odds ratio for first vs. fourth quartile: happiness = 1.17, 95% CI = 1.001, 1.36; ikigai = 1.42, 95% CI = 1.20, 1.67). More snow-removal time was inversely associated with happiness and with ikigai in women only (adjusted odds ratio for first vs. fourth quartile: happiness = 0.75, 95% CI = 0.67, 0.85; ikigai = 0.78, 95% CI = 0.69, 0.88). Our findings showed that physical activity in rural life was associated with happiness and with ikigai, and gender differences were observed in their associations with more snow-removal time. These results may be useful in helping to identify people in rural communities who are vulnerable in terms of psychological well-being.

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Predicting mental health help seeking orientations among diverse Undergraduates: An ordinal logistic regression analysis✰

Cited by 27 publications

References 40 publications

Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting

Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting

Social influence and impact of social media on users’ mobility decisions

Associations of physical activity in rural life with happiness and ikigai: a cross-sectional study

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