Applying Computer Adaptive Testing Methods to Suicide Risk Screening in the Emergency Department.

<bold>Objective: </bold>Combine test theory with technology to develop brief, reliable suicide risk measures in the emergency department.<bold>Methods: </bold>A computer adaptive test for suicide risk was built using the Beck Scale for Suicide Ideation and tested among the emergency department popul...

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Publicado en:Suicide & Life-Threatening Behavior Vol. 49; no. 4; pp. 917 - 928
Autores principales: Boudreaux, Edwin D., De Beurs, Derek P., Nguyen, Tam H., Haskins, Brianna L., Larkin, Celine, Barton, Bruce
Formato: journal article
Publicado: Wiley-Blackwell Aug2019
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Applying Computer Adaptive Testing Methods to Suicide Risk Screening in the Emergency Department.
      aug:
        au:
          Boudreaux, Edwin D.
          De Beurs, Derek P.
          Nguyen, Tam H.
          Haskins, Brianna L.
          Larkin, Celine
          Barton, Bruce
        affil:
          Departments of Emergency Medicine, Psychiatry, and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester MA, USA
          Netherlands Institute for Health Services Research (NIVEL), Utrecht The Netherlands
          Department of Clinical Psychology, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam The Netherlands
          Connell School of Nursing, Boston College, Chestnut Hill MA, USA
          Department of Emergency Medicine, University of Massachusetts Medical School, Worcester MA, USA
          Quantitative Health Sciences, University of Massachusetts Medical School, Worcester MA, USA
      su:
        Massachusetts
        National Institutes of Health (U.S.)
        Suicide
        Computer simulation
        Suicidal ideation
        Emergency medical services
        Psychological tests
        Medical screening
        Computer adaptive testing
        Hospital emergency services
        Test methods
        Risk assessment
        Experimental design
        Computer-aided diagnosis
      sug:
        subj:
          Suicide
          Computer simulation
          Suicidal ideation
          Emergency medical services
          Psychological tests
          Medical screening
          Massachusetts
          National Institutes of Health (U.S.)
          Emergency and Other Relief Services
          Municipal police services
          All Other Miscellaneous Ambulatory Health Care Services
          Computer adaptive testing
          Hospital emergency services
          Test methods
          Risk assessment
          Experimental design
          Computer-aided diagnosis
      ab: <bold>Objective: </bold>Combine test theory with technology to develop brief, reliable suicide risk measures in the emergency department.<bold>Methods: </bold>A computer adaptive test for suicide risk was built using the Beck Scale for Suicide Ideation and tested among the emergency department population. Data were analyzed from a sample of 1,350 patients in several Massachusetts emergency departments. The test was built as outlined by the National Institutes of Health Patient-Reported Outcomes Measurement Information System.<bold>Results: </bold>Of 1,350 patients, 74 (5%) scored above the cutoff of BSS > 2. Item 2, "Wish to die", was the most informative item. When using only Item 2, 20% (n = 15/74) of at-risk patients and 3% (n = 40/1,276) of not-at-risk patients were misclassified. Patients were classified after four items with computer adaptive testing trait estimates highly comparable to those of the full scale. The precision rule model did not reduce the scale.<bold>Conclusions: </bold>This study models the creation of a computer adaptive test for suicide ideation and marks the start of the development of computer adaptive tests as a novel suicide risk screening tool in the emergency department. Computer adaptive tests hold promise for revolutionizing behavioral health screening by addressing barriers including time and knowledge deficits.
      pubtype: Academic Journal
      doctype: journal article
      src: R
    language: English
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