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Record W2984367252 · doi:10.24869/psyd.2019.473

ALEXITHYMIA AND PSYCHOLOGICAL DISTRESS AMONG WOMEN UNDERGOING IN VITRO FERTILIZATION

2019· article· en· W2984367252 on OpenAlexaboutno aff
Dunja Jurić Vukelić, Zorana Kušević, Jasminka Horvatić

Bibliographic record

VenuePsychiatria Danubina · 2019
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaAnxietyToronto Alexithymia ScaleDepression (economics)Clinical psychologyPsychological distressDistressIn vitro fertilisationPsychologyMedicinePsychiatryPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the current study was to analyze the relationship between alexithymia, anxiety, physical problems, trauma, and psychological distress in women undergoing in vitro fertilization. SUBJECTS AND METHODS: The study was based on 78 women (mean age = 34.4 ys) who were referred to a fertility treatment with in vitro fertilization. The questionnaires (socio-demographic questionnaire, Toronto Alexithymia Scale, and Clinical Outcomes in Routine Evaluation - Outcome Measure) were administered by the investigators. RESULTS: Our results suggest that alexithymia was significantly correlated to anxiety (r=0.506, p=0.00), depression (r=0.591, p=0.00), physical problems (r=0.477, p=0.00), trauma (r=0.512, p=0.00), and psychological distress (r=0.598, p=0.00). Furthermore, high alexithymia group showed significantly higher levels of anxiety (F=4.65, p=0.00), depression (F=2.30, p=0.00), trauma (F=1.80, p=0.00) and general psychological distress (F=2.85, p=0.04) than the low alexithymia group. CONCLUSIONS: Results of the present study point out that alexithymia could be considered a potential risk factor for high levels of anxiety, depression and general psychological distress. It may also be used as an indicator of a need for further psychological support aimed at women undergoing in vitro fertilization.

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)0.000
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 score0.014
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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