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Record W2160128646 · doi:10.2190/il.18.2.f

Somatization of Loss of Fatherhood: A Case Study of a Chinese Man with Major Depression

2010· article· en· W2160128646 on OpenAlexaboutno aff
Yuming Lai, Yu‐Ting Chen, Fei‐Hsiu Hsiao

Bibliographic record

VenueIllness Crisis & Loss · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSomatizationWifeFeelingGriefDepression (economics)AngerPsychological painPsychologyLonelinessDistressAlexithymiaPresentation (obstetrics)PsychotherapistPsychiatryClinical psychologyAnxietyMedicineSocial psychology

Abstract

fetched live from OpenAlex

This case study reports on a 52-year-old man suffering from depression which was largely precipitated by loss of fatherhood after divorce when his ex-wife took their son and migrated to Canada. In the presentation of his distress to the therapist, he mainly complained of “heart ache,” which could not be confirmed by a number of physicians as having any kind of physical disorder. The sense of psychological pain due to the loss of the love relationship with his son was hidden and was expressed in physical form. His self-identify as a strong man and as a manager of a pharmacy company were main barriers for him to reveal his psychological pain. His feelings of anger toward his ex-wife and grief from the separation from his son began to occur during an 8-week body-mind-spirit group therapy. Biological, psychological, and social aspects of pain were integrated into the healing process.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.336
Teacher spread0.326 · 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 designCase report
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

Citations2
Published2010
Admission routes1
Has abstractyes

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