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Record W2614806712

Effects of a positive psychological intervention on the mental health and happiness of patients undergoing in vitro fertilization

2017· article· en· W2614806712 on OpenAlexaboutno aff
Suping Zhang, Hongli Zhang, Zhongjun Qiu, Huiyan Tang

Bibliographic record

VenueBiomedical Research-tokyo · 2017
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessSymptom Checklist 90Mental healthAnxietyPregnancyMedicinePsychological interventionDepression (economics)ChecklistAffect (linguistics)Clinical psychologyPsychologyInternal medicineObstetricsPhysical therapyPsychiatryPsychotherapistBiology
DOInot available

Abstract

fetched live from OpenAlex

The aims of this study were to investigate the effects of a positive psychological intervention on the mental health and happiness of patients undergoing In vitro fertilization (IVF), and to observe the pregnancy outcome of the IVF patients. Two hundred infertile women requiring IVF were randomly divided into an intervention group (Group I) and a control group (Group C), with 100 patients in each group. Both groups were given routine treatment and IVF nursing. Group I was further provided with a positive psychological intervention. The Symptom Checklist-90 (SCL-90) and Memorial University of Newfoundland Scale of Happiness were used to evaluate the mental health and happiness of patients. The difference in the pregnancy outcome between the two groups was also observed. The total SCL-90 score, total average score, number of positive items, interpersonal sensitivity score, depression score, and anxiety score in Group I were significantly lower than in Group C (P<0.05). The total happiness score, positive affect score, and positive experience score in Group I were significantly higher than in Group C (P<0.01), whereas the negative affect score and negative experience score in Group I were significantly lower than in Group C (P<0.01). Thus, a positive psychological intervention can improve the mental health and happiness of patients undergoing IVF, and improve the clinical pregnancy rate.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.432
Teacher spread0.381 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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