Personal Recovery With Bipolar Disorder: A Network Analysis
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| Formato: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867016910276657152 |
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| author | Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones |
| author_facet | Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones |
| collection | Wiley Open Access |
| contents | Personal Recovery With Bipolar Disorder: A Network Analysis Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones Clinical Psychology & Psychotherapy ABSTRACTBackgroundPersonal 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.MethodsThe 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.ResultsTwo 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.LimitationsThe 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.ConclusionsNetwork 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. 10.1002/cpp.70001 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/cpp.70001 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_cpp_70001 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Personal Recovery With Bipolar Disorder: A Network Analysis Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones Clinical Psychology & Psychotherapy Personal Recovery With Bipolar Disorder: A Network Analysis Zoe Glossop Catriona Campbell Anastasia Ushakova Alyson Dodd Steven Jones Clinical Psychology & Psychotherapy ABSTRACTBackgroundPersonal 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.MethodsThe 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.ResultsTwo 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.LimitationsThe 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.ConclusionsNetwork 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. 10.1002/cpp.70001 http://creativecommons.org/licenses/by/4.0/ |
| title | Personal Recovery With Bipolar Disorder: A Network Analysis |
| topic | Clinical Psychology & Psychotherapy |
| url | https://onlinelibrary.wiley.com/doi/10.1002/cpp.70001 |