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Record W3176079178 · doi:10.1192/bjo.2021.93

Exploring determinants of self-management in adults with severe mental illness: a qualitative evidence synthesis

2021· article· en· W3176079178 on OpenAlexaff
Claire Carswell, Jennifer Valeska Elli Brown, Abisola Balogun, Jo Taylor, Peter Coventry, Charlotte Kitchen, Ian Kellar, Emily Peckham, Sue Bellass, Sarah Alderson, Jennie Lister, Richard I. G. Holt, Catherine Hewitt, Rowena Jacobs, David Shiers, Jan R. Boehnke, Ramzi Ajjan, Najma Siddiqi

Bibliographic record

VenueBJPsych Open · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsYork University
FundersEconomic and Social Research Council
KeywordsCINAHLSelf-managementPsychologyQualitative researchMental illnessPsycINFOMental healthMEDLINEDiabetes managementDisease managementClinical psychologyMedicineGerontologyType 2 diabetesPsychiatryPsychological interventionDiabetes mellitusHealth management systemAlternative medicine

Abstract

fetched live from OpenAlex

Aims To systematically review and synthesise qualitative evidence about determinants of self-management in adults with SMI. The goal is to use findings from this review to inform the design of effective self-management strategies for people with SMI and LTCs. Background People living with serious mental illness (SMI) have a reduced life expectancy by around 15–20 years, mainly due to the high prevalence of long-term physical conditions such as diabetes and heart disease. People with SMI face many challenges when trying to manage their physical health. Little is known about the determinants of self-management – managing the emotional and practical issues – of long-term conditions (LTCs) for people with SMI. Method Six databases, including CINAHL and MEDLINE, were searched to identify qualitative studies that explored people's perceptions about determinants of self-management in adults with SMI (with or without comorbid LTCs). Self-management was defined according to the American Association of Diabetes Educator's self-care behaviours (AADE7). Determinants were defined according to the Capabilities, Opportunity, Motivations and Behaviours (COM-B) framework. Eligible studies were purposively sampled for synthesis according to the richness of the data (assessed using Ames et al (2017)'s data richness scale), and thematically synthesised. Result Twenty-six articles were included in the synthesis. Seven studies focused on self-management of LTCs, with the remaining articles exploring self-management of SMI. Six analytic themes and 28 sub-themes were identified from the synthesis. The themes included: the additional burden of SMI; living with comorbidities; beliefs and attitudes about self-management; support from others for self-management; social and environmental factors; routine, structure and planning. Capabilities for self-management were linked to people's perceptions about the support they received for their SMI and LTC from healthcare professionals, family and friends. Opportunities for self-management were more commonly expressed in the context of social and environmental factors. Motivation for self-management was influenced by beliefs and attitudes, whilst being closely related to the burden of SMI. Conclusion The themes identified from the synthesis suggest that capabilities, opportunities and motivations for self-management can be negatively influenced by the experience of SMI, whilst social and professional support, improved access to resources, and increased involvement in care, could promote self-management. Support programmes for people with SMI and LTCs need to account for these experiences and adapt to meet the unique needs of this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.120
GPT teacher head0.387
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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