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Personal Recovery With Bipolar Disorder: A Network Analysis

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Personal Recovery With Bipolar Disorder: A Network Analysis. / Glossop, Zoe; Campbell, Catriona; Ushakova, Anastasia et al.
In: Clinical Psychology and Psychotherapy, Vol. 31, No. 5, e70001, 23.10.2024.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Glossop Z, Campbell C, Ushakova A, Dodd A, Jones S. Personal Recovery With Bipolar Disorder: A Network Analysis. Clinical Psychology and Psychotherapy. 2024 Oct 23;31(5):e70001. Epub 2024 Oct 23. doi: 10.1002/cpp.70001

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Glossop, Zoe ; Campbell, Catriona ; Ushakova, Anastasia et al. / Personal Recovery With Bipolar Disorder : A Network Analysis. In: Clinical Psychology and Psychotherapy. 2024 ; Vol. 31, No. 5.

Bibtex

@article{1c0fc3b5245340beb646945b99a50058,
title = "Personal Recovery With Bipolar Disorder: A Network Analysis",
abstract = "Background: Personal recovery is valued by people with bipolar disorder (BD), yet its conceptualisation is unclear. Prior work conceptualising personal recovery has focussed on qualitative evidence or clinical factors without considering broader psychosocial factors. This study used a network analysis of Bipolar Recovery Questionnaire (BRQ) responses, aiming to identify (1) independent relationships between items to identify those most “central” to personal recovery and (2) how the relationships between items reflect themes of personal recovery. Methods: The model was developed from BRQ responses (36 items) from 394 people diagnosed with bipolar disorder. The undirected network was based on a partial correlation matrix and was weighted. Strength scores were calculated for each node. Community detection analysis identified potential themes. The accuracy of the network was assessed using bootstrapping. Results: Two consistent communities were identified: “Access to meaningful activity” and “Learning from experiences.” “I feel confident enough to get involved in things in life that interest me” was the strongest item, although the strength stability coefficient (0.36) suggested strength should be interpreted with caution. The average edge weight was 0.02; however, stronger edges were identified. Limitations: The network showed low stability, possibly due to sample heterogeneity. Future work could incorporate demographic variables, such as time since BD diagnosis or stage of personal recovery, into network estimation. Conclusions: Network analysis can be applied to personal recovery, not only clinical symptoms of BD. Clinical applications could include tailoring recovery‐focussed therapies towards encouraging important aspects of recovery, such as feeling confident to get involved with life.",
keywords = "network analysis, personal recovery, bipolar disorder, community detection",
author = "Zoe Glossop and Catriona Campbell and Anastasia Ushakova and Alyson Dodd and Steven Jones",
year = "2024",
month = oct,
day = "23",
doi = "10.1002/cpp.70001",
language = "English",
volume = "31",
journal = "Clinical Psychology and Psychotherapy",
issn = "1063-3995",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - Personal Recovery With Bipolar Disorder

T2 - A Network Analysis

AU - Glossop, Zoe

AU - Campbell, Catriona

AU - Ushakova, Anastasia

AU - Dodd, Alyson

AU - Jones, Steven

PY - 2024/10/23

Y1 - 2024/10/23

N2 - Background: Personal recovery is valued by people with bipolar disorder (BD), yet its conceptualisation is unclear. Prior work conceptualising personal recovery has focussed on qualitative evidence or clinical factors without considering broader psychosocial factors. This study used a network analysis of Bipolar Recovery Questionnaire (BRQ) responses, aiming to identify (1) independent relationships between items to identify those most “central” to personal recovery and (2) how the relationships between items reflect themes of personal recovery. Methods: The model was developed from BRQ responses (36 items) from 394 people diagnosed with bipolar disorder. The undirected network was based on a partial correlation matrix and was weighted. Strength scores were calculated for each node. Community detection analysis identified potential themes. The accuracy of the network was assessed using bootstrapping. Results: Two consistent communities were identified: “Access to meaningful activity” and “Learning from experiences.” “I feel confident enough to get involved in things in life that interest me” was the strongest item, although the strength stability coefficient (0.36) suggested strength should be interpreted with caution. The average edge weight was 0.02; however, stronger edges were identified. Limitations: The network showed low stability, possibly due to sample heterogeneity. Future work could incorporate demographic variables, such as time since BD diagnosis or stage of personal recovery, into network estimation. Conclusions: Network analysis can be applied to personal recovery, not only clinical symptoms of BD. Clinical applications could include tailoring recovery‐focussed therapies towards encouraging important aspects of recovery, such as feeling confident to get involved with life.

AB - Background: Personal recovery is valued by people with bipolar disorder (BD), yet its conceptualisation is unclear. Prior work conceptualising personal recovery has focussed on qualitative evidence or clinical factors without considering broader psychosocial factors. This study used a network analysis of Bipolar Recovery Questionnaire (BRQ) responses, aiming to identify (1) independent relationships between items to identify those most “central” to personal recovery and (2) how the relationships between items reflect themes of personal recovery. Methods: The model was developed from BRQ responses (36 items) from 394 people diagnosed with bipolar disorder. The undirected network was based on a partial correlation matrix and was weighted. Strength scores were calculated for each node. Community detection analysis identified potential themes. The accuracy of the network was assessed using bootstrapping. Results: Two consistent communities were identified: “Access to meaningful activity” and “Learning from experiences.” “I feel confident enough to get involved in things in life that interest me” was the strongest item, although the strength stability coefficient (0.36) suggested strength should be interpreted with caution. The average edge weight was 0.02; however, stronger edges were identified. Limitations: The network showed low stability, possibly due to sample heterogeneity. Future work could incorporate demographic variables, such as time since BD diagnosis or stage of personal recovery, into network estimation. Conclusions: Network analysis can be applied to personal recovery, not only clinical symptoms of BD. Clinical applications could include tailoring recovery‐focussed therapies towards encouraging important aspects of recovery, such as feeling confident to get involved with life.

KW - network analysis

KW - personal recovery

KW - bipolar disorder

KW - community detection

U2 - 10.1002/cpp.70001

DO - 10.1002/cpp.70001

M3 - Journal article

VL - 31

JO - Clinical Psychology and Psychotherapy

JF - Clinical Psychology and Psychotherapy

SN - 1063-3995

IS - 5

M1 - e70001

ER -