In vitro Fertilization-Embryo Transfer Patients with Alexithymia and Its Influencing Factors: A Potential Profile Analysis
Bibliographic record
Abstract
Purpose: This study aims to explore the classification characteristics of alexithymia in patients undergoing in vitro fertilization-embryo transfer (IVF-ET) and analyze the differences among these classifications in female patients, in order to alleviate the patients’ alexithymia and improve their mental health and reproductive quality of life. Methods: A total of 385 patients undergoing IVF-ET were selected through convenience sampling from the Reproductive Endocrinology Clinic of a Grade III A-level obstetrics and gynecology hospital in mainland China between June 2024 and December 2024. Data collection included the general information survey form, the Toronto Alexithymia Scale, and the General Self-efficacy Scale. Latent profile analysis was used to explore the potential categories of alexithymia among patients receiving IVF-ET treatment. Univariate and multiple logistic regression analyses were conducted to identify the factors correlated with the potential profiles. Results: The alexithymia in patients receiving IVF-ET treatment was categorized into three potential groups: low-risk (48.0%), moderate-risk (46.0%), and high-risk alexithymia groups (6.0%). Multiple logistic regression analysis results indicated that educational level, average monthly household income, and self-efficacy are correlated with alexithymia in patients receiving IVF-ET treatment ( P < 0.05). Conclusion: The alexithymia in patients receiving IVF-ET treatment can be categorized into three potential profile types. The clinical medical staff should consider the characteristics of patients and implement targeted interventions for those with lower levels of education, lower average monthly household income, and poorer self-efficacy, in order to reduce the degree of alexithymia. Plain Language Summary: This study aimed to understand how patients undergoing in vitro fertilization-embryo transfer (IVF-ET), struggle to identify and express their emotions (alexithymia) and find ways to improve their mental health and quality of life. From June to December 2024, 385 IVF-ET patients were recruited from a Chinese gynecology hospital, and researchers used questionnaires to gather information on their personal background, emotional recognition ability, and self-confidence. The data analysis showed that patients’ alexithymia could be categorized into low-risk (48%), moderate-risk (46%), and high-risk (6%) groups, and that lower education level, less family income, and lower self-confidence were associated with a higher likelihood of alexithymia in patients. As such, medical staff should consider patients’personal situations and provide special care and support to those with lower education, income, and self-confidence in an effort to assist them in better managing emotions and potentially enhancing their mental well-being. Keywords: In vitro fertilization-embryo transfer, alexithymia, self-efficacy, latent profile analysis, influencing factors
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".