College Student Utilization of a Comprehensive Psychiatric Emergency Program

Mitchell, Sharon; Kader, Md. Sazzad; Haggerty, Melinda Z.; Bakhai, Yogesh; Warren, C.

doi:10.1002/j.2161-1882.2013.00026.x

Cited by 16 publications

(13 citation statements)

References 16 publications

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“…Psychological distress (as measured by Adult Self-Report and the DSM-IV) was the only significant predictor for the clinical group (63,64), whereas depression (as measured by Center for Epidemiological Studies Depression Scale [CES-D] and Patient Health Questionnaire [PHQ-9]), anxiety (as measured by the Beck Anxiety Inventory and PHQ-9), and psychological distress (as measured by the dysphoria domain of the Trauma Symptom Inventory) were each significant predictors of service use among the general (nonclinical) young adult population (26,43,51,60,65). Notably, probable diagnoses of depression (based on CES-D screening) significantly increased the odds of service use in the longer term among the nonclinical group (Table 2).…”

Section: Predisposing Enabling and Need Variablesmentioning

confidence: 99%

Predictors of Mental Health Service Use by Young Adults: A Systematic Review

Dorstyn

Denson

2016

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Objective: The purpose of this review was to systematically evaluate the available heterogeneous research examining determinants of mental health service use among young adults.Methods: Nine electronic databases were searched to identify quantitative studies examining sociodemographic and psychological variables predictive of or associated with mental health service use. Included studies were examined against the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Effect size estimates in the form of odds ratios were calculated and classified according to predisposing, enabling, and need factors, consistent with Andersen's behavioral model of health care.Results: Eighteen studies met all of the inclusion criteria (N=96,297 participants). Studies generally followed the STROBE recommendations for external validity, although limitations in internal validity were noted. Prior service contact and being homosexual or bisexual, female, or Caucasian were predisposing factors significantly associated with mental health service use. Social support was the single enabling factor identified, although this finding was based on limited data. In relation to need, young adults who reported higher perceived need for professional help or more mental health difficulties were more likely to have utilized services.Conclusions: This review identified subgroups of young adults who are less likely to access mental health support. Future research should focus on developing psychoeducational interventions targeted at young men and racial-ethnic minority groups, in addition to informing young adults of the success of mental health counseling in the absence of a diagnosis.

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Section: Predisposing Enabling and Need Variablesmentioning

confidence: 99%

Predictors of Mental Health Service Use by Young Adults: A Systematic Review

Dorstyn

Denson

2016

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“…Indicators of early intensive service use were key variables in predicting higher costs, which is consistent with other studies. [30][31][32] One limitation is the circularity of patients being more likely to receive greater service input if they have already received it previously (for example commencement of psychological therapy in the prediction window naturally predicting its continuation after the index date). There may also be confounding by the disorder severity influencing the level of staff contact.…”

Section: Interpretation Of Our Findingsmentioning

confidence: 99%

Predicting high-cost care in a mental health setting

et al. 2020

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Background The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes. Aims We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation. Method We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves. Results In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%). Conclusions EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.

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“…Most agree that there are situations where hospitalization is warranted and/or should be considered (e.g., Chiles & Strosahl, 2005; Jobes, 2006; Linehan, 1993; Mitchell, Kader, Haggerty, Bakhai, & Warren, 2013). In the absence of clear empirical guidance, however, these decisions must be made on an individual basis as opposed to being made as a matter of policy.…”

Section: The Case Against Hospitalization As a Standard Of Care For Smentioning

confidence: 99%