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Record W2923918738 · doi:10.29252/jrh.9.2.187

Alexithymia as a moderator of the relation between self-care and psychological distress

2019· article· en· W2923918738 on OpenAlexaboutno aff
Mohammad Abassi, Fatemeh Rezaei

Bibliographic record

VenueJournal of Research and Health · 2019
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaModerationToronto Alexithymia ScalePsychologyPsychological distressDistressClinical psychologyAnxietyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Alexithymia is considered as important contributor in the psychological distress.This study examined the moderator role of alexithymia in the relationship between self-care and psychological distress in 217 elementary school teachers.Alexithymia, self-care and psychological distress has been assessed in 217 teachers (108 females and 109 males).Participants were asked to complete 4 including socio-demographic questionnaire, toronto alexithymia scale, health-promoting lifestyle profile II, and the depression, anxiety, and stress scales.Results revealed that there is a significant relationship between self-care, alexithymia and psychological distress.Alexithymia was also a moderator in the relationship between self-care and psychological distress.The findings supported the hypothesis that higher levels of alexithymia would be associated with higher levels of psychological distress, and that lower levels of alexithymia would be associated with lower levels of psychological distress.Alexithymia helped explain the self-care and psychological distress link in adults.

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)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.102
GPT teacher head0.458
Teacher spread0.356 · 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
Published2019
Admission routes1
Has abstractyes

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