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Record W2461523811 · doi:10.1177/1078390316655207

Common Mental Disorders in Primary Health Care Units

2016· article· en· W2461523811 on OpenAlexaff
Tatiana Longo Borges, Adriana Inocenti Miasso, Emilene Reisdofer, Manoel Antônio dos Santos, Kelly Graziani Giacchero Vedana, Kathleen Hegadoren

Bibliographic record

VenueJournal of the American Psychiatric Nurses Association · 2016
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of AlbertaCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineMental healthQuality of life (healthcare)Affect (linguistics)Marital statusIncidence (geometry)Cross-sectional studyPrimary carePsychiatryFamily medicineGerontologyPsychologyPopulationEnvironmental healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Considering the high worldwide prevalence of common mental disorders (CMDs), characterizing the association between CMD and quality of life (QoL) constitute a valuable measure to gauge patient's functional impairment due to CMD symptoms. OBJECTIVE: To investigate factors associated with the incidence of CMD and its impact on the QoL in primary health care (PHC) patients. DESIGN: Cross-sectional study completed in a municipality in Brazil. Standardized tools included the Self-Reporting Questionnaire-20 to detect CMDs and the WHOQOL-brief to assess QoL, in addition to a sociodemographic and treatment-related questionnaire. RESULTS: A total of 41.4% of the patients met cutoff scores for a CMD, and the presence of a CMD was associated with female gender and marital status. Patients with CMDs had lower QoL scores than patients who were negative for a CMD. CONCLUSIONS: CMDs are highly prevalent in PHC settings and affect patients' QoL. The high frequency of CMD in those that seek care through PHC necessitate incorporating mental health services into the range of available services.

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 score0.277
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.342
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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of the American Psychiatric Nurses AssociationSame topicMental Health Treatment and AccessFrench-language works237,207