MétaCan
Menu
Back to cohort
Record W1531144783 · doi:10.1177/070674370304800711

Patient Attitudes regarding Causes of Depression: Implications for Psychoeducation

2003· article· en· W1531144783 on OpenAlexaffvenue
Janaki Srinivasan, Nicole L. Cohen, Sagar V. Parikh

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2003
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of TorontoYork UniversityUniversity Health NetworkCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychoeducationDepression (economics)PsychiatryClinical psychologyPsychologyCognitionMajor depressive disorderIntervention (counseling)

Abstract

fetched live from OpenAlex

OBJECTIVE: Patient attitudes toward mental illness are an important determinant of treatment compliance and treatment outcome. A patient's age, sex, style of thinking, lifestyle, and beliefs all may influence perceptions. This study aimed to determine patient attitudes. METHOD: Patients with a depressive disorder (n = 102) who were referred for psychiatric consultation and treatment to a community general hospitial psychiatric outpatient clinic completed a 9-item self-report questionnaire to determine their perceptions of the biological, psychological, cognitive, and spiritual causes of their depressive disorder. RESULTS: Women were more likely to endorse their depressive disorder as related to a biological abnormality. With respect to age, older individuals were less likely to identify cognitive factors and loss of spirituality as causal factors in their depression. CONCLUSIONS: A relation exists between demographic variables, including sex and age, and beliefs about causes of depression and related disorders. These findings have implications for refining patient psychoeducation.

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)0.004
metaresearch head score (Gemma)0.019
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.047
GPT teacher head0.380
Teacher spread0.333 · 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

Citations45
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of PsychiatrySame topicMental Health Treatment and AccessFrench-language works237,207