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Record W3014475022 · doi:10.1371/journal.pone.0231033

Analyses of medical coping styles and related factors among female patients undergoing in vitro fertilization and embryonic transfer

2020· article· en· W3014475022 on OpenAlexaboutno aff
Liwen Shen

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCoping (psychology)FeelingSocial supportIn vitro fertilisationAvoidance copingInfertilityClinical psychologyMedicinePsychological interventionPsychologyPregnancyPsychiatryPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigated the medical coping styles of female patients treated with in vitro fertilization and embryonic transfer (IVF-ET), and analyzed the effects of alexithymia and social support on their choice of coping style. METHODS: A survey was conducted with 285 female patients undergoing IVF-ET in a reproductive medical center of a third-grade class-A hospital in China using the Medical Coping Modes Questionnaire, the Social Support Rating Scale, and the Toronto Alexithymia scale. RESULTS: Patients who underwent IVF-ET treatment had a higher score for avoidance as a coping mode than did normal controls. Utilization of social support predicted the use of confrontation as a coping style. Difficulty identifying feelings, objective support, and utilization of social support were factors in the choice of avoidance as a coping style, and length of infertility treatment, difficulty identifying feelings, and subjective support predicted patients' use of the acceptance-resignation as a coping style. CONCLUSION: Patients who undergo IVF-ET generally select the coping style of avoidance, which is not conducive to treatment. Targeted intervention strategies should be developed based on the factors influencing patients' choice of coping style(s) to guide them in choosing positive coping methods, improve compliance, and achieve successful pregnancy outcomes.

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)0.001
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 score0.178
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.109
GPT teacher head0.311
Teacher spread0.201 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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