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Record W2054770184 · doi:10.1155/2012/724848

Stigma and Discrimination in People Suffering with a Mood Disorder: A Cross-Sectional Study

2012· article· en· W2054770184 on OpenAlexaff
Lauren Lazowski, Michelle Koller, Heather Stuart, Roumen Milev

Bibliographic record

VenueDepression Research and Treatment · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychosocialBipolar disorderPsychiatryMedicineStigma (botany)MoodDepression (economics)Clinical psychologySocial stigmaPopulationMood disordersHuman immunodeficiency virus (HIV)AnxietyFamily medicine

Abstract

fetched live from OpenAlex

Background. Much research is done on the stigma of mental illness, but little research has been done to characterize these phenomena from the perspective of people with mood disorders. Objective. To characterize the extent to which individuals with bipolar disorder and depression are stigmatized, determine factors related to higher levels of stigmatization, and assess the reliability of the Inventory of Stigmatizing Experiences in a population of people with a mood disorder. Methods. Two hundred and fourteen individuals with depression and bipolar disorder were recruited from a tertiary care psychiatric hospital and surveyed using the Inventory of Stigmatizing Experiences. Results. Participants reported high levels of stigma experiences and this did not differ by diagnosis (P = 0.578). However, people with bipolar disorder reported greater psychosocial impact of stigma on themselves and their family members compared to people with depression (P = 0.019). The two subscales produced internally consistent results with both populations. Conclusion. Stigma negatively affects those with both depression and bipolar disorder but appears to have a greater psychosocial impact on those with bipolar disorder.

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 score0.030
Threshold uncertainty score0.980

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 head0.111
GPT teacher head0.481
Teacher spread0.370 · 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

Citations40
Published2012
Admission routes1
Has abstractyes

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