Effects of a positive psychological intervention on the mental health and happiness of patients undergoing in vitro fertilization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".