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Record W2101938505 · doi:10.1177/1077801207307801

Cognitive and Emotional Processing in Narratives of Women Abused by Intimate Partners

2007· article· en· W2101938505 on OpenAlexaff
Danielle Holmes, Georg W. Alpers, Tasneem Ismailji, Catherine Classen, Talor Wales, Valerie Cheasty, Andrew F. Miller, Cheryl Koopman

Bibliographic record

VenueViolence Against Women · 2007
Typearticle
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsNarrativeCognitionPsychologyClinical psychologyDepression (economics)Session (web analytics)Suicide preventionPoison controlHuman factors and ergonomicsInjury preventionDepressive symptomsPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

This study examined relationships between cognitive and emotional processing with changes in pain and depression among intimate partner violence survivors. Twenty-five women who wrote about their most traumatic experiences completed measures of pain and depressive symptoms before the first writing session and again 4 months following the last writing session. Reduced pain was significantly associated with less use of positive and negative emotion words. Relationships between cognitive and emotional aspects of writing with changes in depressive symptoms fell short of statistical significance. The results suggest that emotional processing in narrative writing predicts changes in pain in intimate partner violence survivors.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.369
Teacher spread0.345 · 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 designQualitative
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

Citations86
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueViolence Against WomenSame topicMental Health via WritingFrench-language works237,207