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Record W2775348582 · doi:10.1111/eip.12532

The score distribution and factor structure of the Community Assessment of Psychic Experiences‐Positive Scale (CAPE‐P15) in a Canadian sample

2017· article· en· W2775348582 on OpenAlexaffabout
Mashal K. Haque, Jill A. Jacobson, Christopher R. Bowie, Kevin G. Munhall

Bibliographic record

VenueEarly Intervention in Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyClinical psychologyConfirmatory factor analysisPopulationSchizophrenia (object-oriented programming)Scale (ratio)Sample (material)Intervention (counseling)PsychiatryMedicineStructural equation modelingGeographyEnvironmental healthCartographyStatistics

Abstract

fetched live from OpenAlex

AIM: Psychotic-like experiences (PLEs) share several risk factors with psychotic disorders and confer greater risk of developing a psychotic disorder. Thus, individuals with PLEs not only comprise a valuable population in which to study the aetiology and premorbid changes associated with psychosis, but also represent a high-risk population that could benefit from clinical monitoring or early intervention efforts. METHOD: We examined the score distribution and factor structure of the current 15-item Community Assessment of Psychic Experiences-Positive Scale (CAPE-P15) in a Canadian sample. The CAPE-P15, which measures current PLEs in the general population, was completed by 1741 university students. RESULTS: The distribution of total scores was positively skewed, and confirmatory factor analysis indicated that a 3-factor structure produced the best fit. CONCLUSION: The CAPE-P15 has a similar score distribution and consistently measures three types of positive PLEs: persecutory ideation, bizarre experiences and perceptual abnormalities when administered in Canada vs Australia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.680

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 head0.020
GPT teacher head0.349
Teacher spread0.330 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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