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Comorbilidad entre el distrés psicologico y el abuso de drogas en pacientes en centros de tratamiento, en la ciudad de Leon - Nicaragua: implicaciones para políticas y programas

2012· article· es· W1594740445 on OpenAlexaff

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2012
Typearticle
Languagees
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsMental healthSchizophrenia (object-oriented programming)Scale (ratio)Mental disease

Abstract

fetched live from OpenAlex

La presencia de trastornos de salud mental y consumo de drogas ambos se conocen como comorbilidad. Este estudio tuvo como objetivo determinar la prevalencia de la comorbilidad entre el distrés psicológico y el abuso/dependencia de drogas en pacientes de Centros de tratamiento en León, Nicaragua. La mayoría de participantes del estudio eran hombres y 68.5% eran menor de 40 años, solteros (58.5%), con bajo nivel de educación, trabajo de forma autónoma (51.2%), y 41.5% viven con su madre biológica. El 65.9% de los pacientes llegaron al centro por cuenta propia. Las drogas más consumida fueron alcohol, marihuana y crack (59.7%, 58.2% y 53.7%). Los trastornos concurrente más comunes fueron, ansiedad (29.3%), depresión (24.1%), esquizofrenia 9.8% y trastorno bipolar 2,4% y según la escala de Kessler K-10, el 56% de los participantes fueron diagnosticados con distrés psicológico severo. Según la escala de APGAR-familiar, el 26% percibieron familias como disfuncionales.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.323
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.181
GPT teacher head0.462
Teacher spread0.280 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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