MétaCan
Menu
Back to cohort

The Alexithymia Personality Dimension

2012· reference-entry· en· W1614527970 on OpenAlexaff
Graeme J. Taylor, R. Michael Bagby

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAlexithymiaPsychologyPersonalityClinical psychologyTraitFeelingTemperamentConstruct (python library)Coping (psychology)Big Five personality traitsPersonality disordersPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Alexithymia is a dimensional personality trait characterized by difficulties in identifying and describing subjective feelings, a limited imaginal capacity, and an externally oriented cognitive style. In this chapter we provide an extensive review of empirical research on alexithymia and conclude that there is strong support for the validity, stability (reliability), and dimensional nature of the construct. We review evidence indicating that this construct is distinct from DSM-based personality disorders, and from dimensional personality traits and temperament. Consistent with clinical reports, alexithymia is associated with several common medical and psychiatric disorders, influences the outcome of insight-oriented psychotherapy, and can adversely affect response to some medical treatments. Although longitudinal studies are needed to establish whether alexithymia is a risk factor for medical and psychiatric disorders, individuals with a high degree of alexithymia have insecure attachments to others and employ maladaptive defenses and coping styles that are illness risk factors themselves. More planning and research are needed to develop and evaluate the effectiveness of therapies aimed at reducing alexithymia.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations66
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207