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Record W2112513052 · doi:10.1186/1752-4458-6-16

Improving care and wellness in bipolar disorder: origins, evolution and future directions of a collaborative knowledge exchange network

2012· article· en· W2112513052 on OpenAlexaff
Erin E. Michalak, Rachelle Hole, James Livingston, Greg Murray, Sagar V. Parikh, Sara Lapsley, Sally McBride

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2012
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsToronto Western HospitalProvincial Health Services AuthorityTRIUMFUniversity of British Columbia HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialMultidisciplinary approachParticipatory action researchCommunity-based participatory researchKnowledge translationVariety (cybernetics)Bipolar disorderCitizen journalismHealth careSociologyPsychologyKnowledge managementPsychiatrySocial sciencePolitical scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The Collaborative RESearch team to study psychosocial factors in bipolar disorder (CREST.BD) is a multidisciplinary, cross-sectoral network dedicated to both fundamental research and knowledge exchange on bipolar disorder (BD). The core mission of the network is to advance the science and understanding of psychological and social issues associated with BD, improve the care and wellness of people living with BD, and strengthen services and supports for these individuals. CREST.BD bridges traditional and newer research approaches, particularly embracing community-based participatory research (CBPR) methods. Membership of CREST is broad, including academic researchers, people with BD, their family members and supports, and a variety of health care providers. Here, we describe the origins, evolution, approach to planning and evaluation and future vision for our network within the landscape of CBPR and integrated knowledge translation (KT), and explore the keys and challenges to success we have encountered working within this framework.

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: none
Teacher disagreement score0.679
Threshold uncertainty score0.313

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 communication0.0000.000
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 head0.009
GPT teacher head0.305
Teacher spread0.297 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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