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Record W2591923171

Mental Health Literacy and Ontario Young People: Major Depressive Disorder, Bipolar Disorder, and Generalized Anxiety Disorder

2016· dissertation· en· W2591923171 on OpenAlexaboutno aff
Christine Jayme Manser

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2016
Typedissertation
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBipolar disorderGeneralized anxiety disorderPsychiatryMental healthPrevalence of mental disordersPsychologyAnxietyMajor depressive disorderMental health literacyClinical psychologyMental illnessMood
DOInot available

Abstract

fetched live from OpenAlex

This study examined the mental health literacy of a group of young people, with particular
\ninterest to their ability to correctly label, identify symptoms, and recommend appropriate help
\nsources for Major Depressive Disorder, Generalized Anxiety Disorder, and Bipolar Disorder.
\nRespondents were 88 Ontario, Canada residents (26 males, 62 females) aged 18-24. Respondents
\nprovided mixed knowledge in ability to determine whether an individual was dealing with
\nmental illness as well as mixed knowledge in labeling the mental disorders examined and
\nidentifying the symptoms of each disorder. Respondents were significantly more likely to
\ncorrectly label Major Depressive Disorder opposed to Generalized Anxiety Disorder and Bipolar
\nDisorder. As well, respondents were significantly more likely to label appropriate symptoms for
\nMajor Depressive Disorder opposed to Generalized Anxiety Disorder and Bipolar Disorder. Our
\nfindings suggest that young people have a greater mental health literacy for Major Depressive
\nDisorder opposed to Generalized Anxiety Disorder and Bipolar Disorder. Results are discussed
\nin light of prior adolescent and young adult mental health literacy and clinical implications.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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