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Record W2041876448 · doi:10.1016/j.clysa.2015.01.002

Psychological predictor variables of emotional maladjustment in infertility: Analysis of the moderating role of gender

2015· article· en· W2041876448 on OpenAlexaboutno aff
Isabel Ramírez-Uclés, M. Del Castillo-Aparicio

Bibliographic record

VenueClínica y Salud · 2015
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
FundersUniversidad Nacional de Educación a Distancia
KeywordsPsychologyAlexithymiaAnxietyAffect (linguistics)ModerationClinical psychologyInfertilityTraitInterpersonal communicationMultilevel modelDevelopmental psychologySocial psychologyPsychiatryPregnancy

Abstract

fetched live from OpenAlex

The objective of this study is to find out if the variables state-anxiety, trait-anxiety, positive-affect, negative-affect, alexithymia, and adaptive (personal and interpersonal) resources can predict emotional maladjustment in infertile people, taking into account the potentially moderating role of gender. A sample of 101 participants with an infertility diagnosis (51 males and 50 females) completed a battery of psychological tests (DERA, Emotional Maladjustment and Adaptive Resources in Infertility questionnaire, State-Trait Anxiety Inventory [STAI], PANAS, Positive and Negative Affect Schedule, and TAS-20, [Toronto Alexithymia Scale]). The moderating, partial, and interactive effects of the variables were analyzed using hierarchical regression analysis. The resulting model explained 71.1% of total variance, resulting in gender as an important moderating variable and trait anxiety, state anxiety, negative affect, and low interpersonal resources as strong predictors of emotional maladjustment in infertile people. These results provide guidance in selecting the most appropriate psychological support and treatment for the emotional adjustment of infertile women and men.

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)0.001
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 score0.003
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.094
GPT teacher head0.356
Teacher spread0.262 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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