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Record W2038312366 · doi:10.1111/bdi.12258

Optimizing delivery of recovery‐oriented online self‐management strategies for bipolar disorder: a review

2014· review· en· W2038312366 on OpenAlexaff
Nuwan Leitan, Erin E. Michalak, Lesley Berk, Michael Berk, Greg Murray

Bibliographic record

VenueBipolar Disorders · 2014
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBipolar disorderPsychosocialSelf-managementMental healthPsychologyEmpowermentPerspective (graphical)PsychotherapistKnowledge managementComputer sciencePsychiatryCognitionPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: Self-management is emerging as a viable alternative to difficult-to-access psychosocial treatments for bipolar disorder (BD), and has particular relevance to recovery-related goals around empowerment and personal meaning. This review examines data and theory on BD self-management from a recovery-oriented perspective, with a particular focus on optimizing low-intensity delivery of self-management tools via the web. METHODS: A critical evaluation of various literatures was undertaken. Literatures on recovery, online platforms, and self-management in mental health and BD are reviewed. RESULTS: The literature suggests that the self-management approach aligns with the recovery framework. However, studies have identified a number of potential barriers to the utilization of self-management programs for BD and it has been suggested that utilizing an online environment may be an effective way to surmount many of these barriers. CONCLUSIONS: Online self-management programs for BD are rapidly developing, and in parallel the recovery perspective is becoming the dominant paradigm for mental health services worldwide, so research is urgently required to assess the efficacy and safety of optimization methods such as professional and/or peer support, tailoring and the development of 'online communities'.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.383
Teacher spread0.345 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2014
Admission routes1
Has abstractyes

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