Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students.
dc.citation.issue | 5 | |
dc.citation.volume | 54 | |
dc.contributor.author | Hasking PA | |
dc.contributor.author | Robinson K | |
dc.contributor.author | McEvoy P | |
dc.contributor.author | Melvin G | |
dc.contributor.author | Bruffaerts R | |
dc.contributor.author | Boyes ME | |
dc.contributor.author | Auerbach RP | |
dc.contributor.author | Hendrie D | |
dc.contributor.author | Nock MK | |
dc.contributor.author | Preece DA | |
dc.contributor.author | Rees C | |
dc.contributor.author | Kessler RC | |
dc.coverage.spatial | England | |
dc.date.accessioned | 2024-10-04T01:01:51Z | |
dc.date.available | 2024-10-04T01:01:51Z | |
dc.date.issued | 2024-04-01 | |
dc.description.abstract | BACKGROUND: Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk. METHODS: Data come from several waves of a prospective cohort study (2016-2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00-19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group. RESULTS: 5454 students ranging in age from 17-36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93-36.07]; Specificity = 97.46 [95% CI 96.21-98.38], PPV = 53.06 [95% CI 40.16-65.56]; AUC range: 0.895 [95% CIs 0.872-0.917] to 0.966 [95% CIs 0.939-0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort. CONCLUSIONS: Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students. | |
dc.description.confidential | false | |
dc.edition.edition | April 2024 | |
dc.format.pagination | 971-979 | |
dc.identifier.author-url | https://www.ncbi.nlm.nih.gov/pubmed/37732419 | |
dc.identifier.citation | Hasking PA, Robinson K, McEvoy P, Melvin G, Bruffaerts R, Boyes ME, Auerbach RP, Hendrie D, Nock MK, Preece DA, Rees C, Kessler RC. (2024). Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students.. Psychol Med. 54. 5. (pp. 971-979). | |
dc.identifier.doi | 10.1017/S0033291723002714 | |
dc.identifier.eissn | 1469-8978 | |
dc.identifier.elements-type | journal-article | |
dc.identifier.issn | 0033-2917 | |
dc.identifier.pii | S0033291723002714 | |
dc.identifier.uri | https://mro.massey.ac.nz/handle/10179/71604 | |
dc.language | eng | |
dc.publisher | Cambridge University Press | |
dc.publisher.uri | https://www.cambridge.org/core/journals/psychological-medicine/article/development-and-evaluation-of-a-predictive-algorithm-and-telehealth-intervention-to-reduce-suicidal-behavior-among-university-students/BCDACC91A454665076D05FA2323B9C38# | |
dc.relation.isPartOf | Psychol Med | |
dc.rights | (c) 2023 The Author/s | |
dc.rights | CC BY 4.0 | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | algorithm | |
dc.subject | predictive risk | |
dc.subject | retrospective cohort trial | |
dc.subject | suicide prevention | |
dc.subject | tertiary education | |
dc.subject | treatment access | |
dc.subject | Humans | |
dc.subject | Female | |
dc.subject | Male | |
dc.subject | Suicidal Ideation | |
dc.subject | Prospective Studies | |
dc.subject | Universities | |
dc.subject | Students | |
dc.subject | Algorithms | |
dc.subject | Telemedicine | |
dc.subject | Risk Factors | |
dc.title | Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students. | |
dc.type | Journal article | |
pubs.elements-id | 487185 | |
pubs.organisational-group | Other |
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