Developing a single‐session outcome measure using natural language processing on digital mental health transcripts.
Background: Current outcome measures in digital mental health lack granularity, especially for single‐session interventions. This study aimed to address this by utilising natural language processing (NLP) methods to create a clear and relevant outcome measure. This paper describes the development of...
| Publicado en: | Counselling & Psychotherapy Research Vol. 24; no. 3; pp. 1057 - 1069 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Sep2024
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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=178280001&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178280001 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14733145 TFX jtl: Counselling & Psychotherapy Research issn: 14733145 maglogo: Y pubinfo: dt: Sep2024 vid: 24 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 178280001 177531708 178280001 178280001 10.1002/capr.12766 178280001 ppf: 1057 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Developing a single‐session outcome measure using natural language processing on digital mental health transcripts. aug: au: Milligan, Gregor Bernard, Aynsley Dowthwaite, Liz Vallejos, Elvira Perez Davis, Jamie Salhi, Louisa Goulding, James affil: N/LAB, Nottingham University Business School, University of Nottingham, Nottingham, UK sug: subj: Natural Language Processing Digital Health Mental Health Services Health Resource Utilization Health Services Needs and Demand Human Adult Mental Health Psychiatric Patients Descriptive Statistics Comparative Studies Funding Source Adult: 19-44 years ab: Background: Current outcome measures in digital mental health lack granularity, especially for single‐session interventions. This study aimed to address this by utilising natural language processing (NLP) methods to create a clear and relevant outcome measure. This paper describes the development of the Adult Session Wants and Needs Outcome Measure (Adult SWAN‐OM), a novel outcome measure for the Qwell digital mental healthcare platform to understand service user (SU) needs engaging in single‐session therapy (SST). Methods: The research employs a multi‐phased approach combining NLP methods with the typical stages of outcome measures development as follows: (1) assumption definition and validation with SUs and clinicians; (2) transcript theme extraction using the RoBERTa large language model (LLM) in conjunction with topic modelling to extract themes from 254 single‐session transcripts from 192 SUs; (3) clinical item refinement focus group; (4) content validity with clinicians and SUs to improve the relevance and clarity of the items; and (5) outcome measure finalisation in a workshop held with clinicians to consolidate the final wording. Results: Ninety‐six potential wants and needs were generated and distilled into 12 measure items. The outcome measure was shown to be relevant and clear to both SUs and clinicians when used in the context of SST. Conclusion: This study highlights the potential of combining NLP approaches with co‐creation methods in single‐session outcome measure development. We argue that the incorporation of clinical expertise and SU experience ensures the clarity and applicability of such measures and that this approach to capturing single‐session wants and needs promises novel insights for supporting digital mental health interventions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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