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Internet use by patients with bipolar disorder: Results from an international multisite survey

2016· article· en· W2414996043 on OpenAlexaff
Rita Bauer, Jörn Conell, Tasha Glenn, Martin Alda, Raffaella Ardau, Bernhard T. Baune, Michael Berk, Yuly Bersudsky, Amy C. Bilderbeck, Alberto Bocchetta, Letizia Bossini, Angela Marianne Paredes Castro, Eric YW. Cheung, Caterina Chillotti, Sabine Choppin, Maria Del Zompo, Rodrigo da Silva Dias, Seetal Dodd, Anne Duffy, Bruno Étain, Andrea Fagiolini, Miryam Fernández Hernández, Julie Garnham, John Geddes, Jonas Gildebro, Ana González‐Pinto, Guy M. Goodwin, Paul Grof, Hirohiko Harima, Stefanie Hassel, Chantal Henry, Diego Hidalgo‐Mazzei, Vaisnvy Kapur, Girish Kunigiri, Beny Lafer, Erik Roj Larsen, Ute Lewitzka, Rasmus Wentzer Licht, Anne Hvenegaard Lund, Błażej Misiak, Scott Monteith, Rodrigo Muñoz, Takako Nakanotani, René Ernst Nielsen, Claire O’Donovan, Y Okamura, Yamima Osher, Patryk Piotrowski, Andreas Reif, Philipp Ritter, Janusz Rybakowski, Kemal Sagduyu, Brett Sawchuk, Elon Schwartz, Ângela Miranda Scippa, Claire Slaney, Ahmad Hatim Sulaiman, Kirsi Suominen, Aleksandra Suwalska, Peter Tam, Yoshitaka Tatebayashi, Leonardo Tondo, Eduard Vieta, Maj Vinberg, Biju Viswanath, Julia Volkert, Mark Zetin, Peter C. Whybrow, Michael Bauer

Bibliographic record

VenuePsychiatry Research · 2016
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsCentre for Movement DisordersUniversity of TorontoUniversity of CalgaryUniversity of OttawaDalhousie University
FundersNational Institute for Health and Care Research
KeywordsThe InternetBipolar disorderOddsPsychiatryDemographicsOdds ratioFeelingMedicinePsychologyFamily medicineDemographyCognitionSocial psychologyInternal medicineSociologyWorld Wide Web

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.052
GPT teacher head0.351
Teacher spread0.300 · 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

Citations47
Published2016
Admission routes1
Has abstractno

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