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Record W2313988132 · doi:10.2202/1553-3840.1239

Complementary and Alternative Medicine in Psychotic Disorders

2010· article· en· W2313988132 on OpenAlexaffabout
Monica Hazra, Samuel Noh, Heather Boon, Andree Taylor, Karen Moss, David C. Mamo

Bibliographic record

VenueJournal of Complementary and Integrative Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineAnxietyPopulationMood disordersPsychiatryAlternative medicineAddictionMoodSchizophrenia (object-oriented programming)Public healthFamily medicineMental healthEnvironmental health

Abstract

fetched live from OpenAlex

The use of complementary and alternative medicine (CAM), including alternative therapies (ALT) and natural health products (NHP) such as vitamin and herbal supplements, is increasingly accepted in both the general population as well as in patients with mood and anxiety disorders. The level of acceptance and use of CAM, however, is unknown among patients being treated for psychotic disorders. Psychotic patients were surveyed about their use of and attitudes toward CAM. Questions included basic demographic and socio-economic items as well as the lifetime and 12-month use of CAM. Data were collected from June to October 2005. A sample of 172 participants representing 8.4% of the total eligible population of the outpatient clinics within the Schizophrenia Program at the Centre for Addiction and Mental Health in Toronto Canada completed the survey. Considering all forms of CAM, the lifetime and 12-month prevalence rate were 88% and 68%, respectively. The use and perceived safety of CAM by this population is similar to that reported by the general population. Clinical and public health implications of these findings are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.033
GPT teacher head0.368
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2010
Admission routes2
Has abstractyes

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