Using machine learning to increase access to and engagement with trauma‐focused interventions for posttraumatic stress disorder.
Background: Post‐traumatic stress disorder (PTSD) poses a global public health challenge. Evidence‐based psychotherapies (EBPs) for PTSD reduce symptoms and improve functioning (Forbes et al., Guilford Press, 2020, 3). However, a number of barriers to access and engagement with these interventions p...
| Publicado en: | British Journal of Clinical Psychology Vol. 64; no. 1; pp. 125 - 137 |
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| Autores principales: | , , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183820188&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183820188 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01446657 99B jtl: British Journal of Clinical Psychology issn: 01446657 maglogo: Y pubinfo: dt: Mar2025 vid: 64 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183820188 177078283 183820188 183820188 10.1111/bjc.12468 183820188 ppf: 125 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using machine learning to increase access to and engagement with trauma‐focused interventions for posttraumatic stress disorder. aug: au: Lenton‐Brym, Ariella P. Collins, Alexis Lane, Jeanine Busso, Carlos Ouyang, Jessica Fitzpatrick, Skye Kuo, Janice R. Monson, Candice M. affil: Nellie Health sug: subj: Stress Disorders, Post-Traumatic Therapy Trauma Machine Learning Utilization Health Services Accessibility Program Evaluation Psychotherapy Methods Medical Practice, Evidence-Based Artificial Intelligence Patient Participation Mental Health Services Patient Dropouts Treatment Outcomes ab: Background: Post‐traumatic stress disorder (PTSD) poses a global public health challenge. Evidence‐based psychotherapies (EBPs) for PTSD reduce symptoms and improve functioning (Forbes et al., Guilford Press, 2020, 3). However, a number of barriers to access and engagement with these interventions prevail. As a result, the use of EBPs in community settings remains disappointingly low (Charney et al., Psychological Trauma: Theory, Research, Practice, and Policy, 11, 2019, 793; Richards et al., Community Mental Health Journal, 53, 2017, 215), and not all patients who receive an EBP for PTSD benefit optimally (Asmundson et al., Cognitive Behaviour Therapy, 48, 2019, 1). Advancements in artificial intelligence (AI) have introduced new possibilities for increasinfg access to and quality of mental health interventions. Aims: The present paper reviews key barriers to accessing and engaging in EBPs for PTSD, discusses current applications of AI in PTSD treatment and provides recommendations for future AI integrations aimed at reducing barriers to access and engagement. Discussion: We propose that AI may be utilized to (1) assess treatment fidelity; (2) elucidate novel predictors of treatment dropout and outcomes; and (3) facilitate patient engagement with the tasks of therapy, including therapy practice. Potential avenues for technological advancements are also considered. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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