Zoe Glossop, Catriona Campbell, Anastasia Ushakova, Alyson Dodd, Steven Jones
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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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Limitations</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":10460,"journal":{"name":"Clinical psychology & psychotherapy","volume":"31 5","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpp.70001","citationCount":"0","resultStr":"{\"title\":\"Personal Recovery With Bipolar Disorder: A Network Analysis\",\"authors\":\"Zoe Glossop, Catriona Campbell, Anastasia Ushakova, Alyson Dodd, Steven Jones\",\"doi\":\"10.1002/cpp.70001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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. 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Personal Recovery With Bipolar Disorder: A Network Analysis
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.
期刊介绍：
Clinical Psychology & Psychotherapy aims to keep clinical psychologists and psychotherapists up to date with new developments in their fields. The Journal will provide an integrative impetus both between theory and practice and between different orientations within clinical psychology and psychotherapy. Clinical Psychology & Psychotherapy will be a forum in which practitioners can present their wealth of expertise and innovations in order to make these available to a wider audience. Equally, the Journal will contain reports from researchers who want to address a larger clinical audience with clinically relevant issues and clinically valid research.
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