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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -