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Comorbilidad entre distrés psicológico y abuso de drogas en pacientes de centros de tratamiento: implicaciones en políticas y programas, Managua - Nicaragua

2012· article· es· W2008095904 on OpenAlexaff
Olga Vladimirovna Kulakova, Robert B. Mann, Carol Strıke, Bruna Brands, Akwatu Khenti

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2012
Typearticle
Languagees
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological distressHumanitiesMedicineGynecologyPsychologyMental healthPsychiatryArt

Abstract

fetched live from OpenAlex

El objetivo de este estudio fue determinar la prevalencia de la comorbilidad entre el distress psicológico y el abuso/dependencia de drogas en los pacientes de los centros de tratamiento de Nicaragua. Se aplicaron cuestionarios EULAC-CICAD en forma de entrevista para describir el perfil del paciente y Kessler 10 para estimar niveles del distress a 130 usuarios de tres Centros de las Organizaciones no Gubernamentales de Managua, Nicaragua. Nivel encontrado de distress psicológico (severo y muy severo), fue de un 35%. También se encontró que un número importante de los pacientes que están siendo tratados actualmente por problemas de salud mental, persisten en experimentar síntomas del distress psicológico. Se concluyó que los hallazgos de este estudio sugieren una revisión sistemática de las políticas y estrategias de intervención y el fortalecimiento de ambos sistemas involucrados, Sistema de Salud y de Drogodependencia.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.170
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.220
GPT teacher head0.508
Teacher spread0.288 · 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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