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Record W4403904069 · doi:10.53555/hj569153

THE IMPACT OF FAMILY STRUCTURE ON PSYCHOLOGICAL DISTRESS IN DISSOCIATIVE CONVULSIONS

2024· article· en· W4403904069 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDissociativePsychologyPsychological distressDistressClinical psychologyDevelopmental psychologyPsychiatryAnxiety

Abstract

fetched live from OpenAlex

Dissociative Convulsions (DC), also known as Psychogenic Nonepileptic Seizures (PNES), are episodes resembling epileptic seizures but without abnormal brain activity. This study investigates the relationship between family structure and psychological distress in patients with DC in the Indian context. A cross-sectional design was used to compare psychological outcomes between 72 patients from joint and nuclear families. Standardized measures, including the Dissociative Experiences Scale-II (DES-II), Cognitive Distortions Questionnaire (CD-Quest), Toronto Alexithymia Scale (TAS-20), and Difficulties in Emotion Regulation Scale (DERS-18), were administered. Statistical analyses involved independent t-tests and Pearson's correlations. Patients from joint families exhibited significantly higher psychological distress, including greater dissociative experiences, cognitive distortions, alexithymia, and emotion regulation difficulties (all p-values < 0.001). These findings highlight the critical role of family dynamics in psychological interventions for DC and underscore the need for culturally sensitive, family-focused treatment approaches.

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)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.419
Teacher spread0.388 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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