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Record W2765315304 · doi:10.4103/ijpsym.ijpsym_411_16

Stressful and Traumatic Experiences among Women with Alcohol Use Disorders in India

2017· article· en· W2765315304 on OpenAlexaff
Kanika Malik, Prabhat Chand, P Marimuthu, L. N. Suman

Bibliographic record

VenueIndian Journal of Psychological Medicine · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsChecklistStressorClinical psychologyPsychiatryMedicineChildhood abusePsychologyAlcohol abuseInterpersonal communicationInterpersonal violenceTraumatic stressInjury preventionPoison controlSexual abuseMedical emergency

Abstract

fetched live from OpenAlex

AIM: The aim of the present study was to examine lifetime stressful and traumatic experiences among women with alcohol use disorders (AUDs). METHODS: The sample comprised of two groups: a clinical group of 35 women with a diagnosis of AUD and a comparison group of 60 women drawn from the community. After screening out, the participants were administered Life Stressor Checklist-Revised. RESULTS: < 0.001). Clinical group reported a high number of childhood abuse and interpersonal traumas across lifespan than comparison group. The relationship between adverse life experiences and alcohol abuse among women was bidirectional. CONCLUSION: Understanding the nature and experiences of trauma in this group has implications for planning gender-sensitive treatment programs for women seeking help for AUDs in India.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.357
Teacher spread0.312 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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