MétaCan
Menu
Back to cohort

Emotional Regulation, Recognition and Attachment among Individuals with Epilepsy and Psychogenic Non-Epileptic Seizures (PNES)

2018· article· en· W2794499955 on OpenAlexaboutno aff
Priyesh Kumar Singh, Tara Singh, Vijaya Nath Mishra, Rameshwar Nath Chaurasia, Ranjeet Singh

Bibliographic record

VenueAsian Journal of Research in Social Sciences and Humanities · 2018
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsychogenic diseaseEpilepsyPsychologyPsychiatryMedicineNeuroscienceClinical psychology

Abstract

fetched live from OpenAlex

Psychogenic Non-Epileptic seizures (PNES) are epilepsy like episodes which have an emotional rather than organic origin. PNES is thought as a result of tension/distress which piles up in individual and shows physical symptoms in form of seizures. Individuals with PNES can neither process, perceive or communicate their emotional problem nor modulate their emotional states in turn developing faulty attachement style. Present study aimed at examining problem in emotion identification, attachment and emotional regulation among epilepsy and PNES patient group. For this purpose 30 participants were selected with well documented PNES (n= 15) and epilepsy (n=15) which were examined with Alexithymia (Toronto Alexithymia Scale), attachment (Relationship Scale questionnaire) and emotional dysregulation (Difficulties in Emotion Regulation Scale) and responses were obtained on three separate administration. Mean scores, standard deviation and ttest was computed. Result shows that there was a significant difference between PNES and epileptic patients group, were PNES patients were more Alexithymic, less emotionally regulated and have faulty attachement style. And professional should consider it during diagnosis and treatment of PNES.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.399
Teacher spread0.281 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAsian Journal of Research in Social Sciences and HumanitiesSame topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207