Current status and influencing factors of emotional suppression in Chinese female breast cancer patients
Bibliographic record
Abstract
Objective: To analyze the level of emotional suppression and the factors influencing it in female breast cancer patients in Henan Province, China, and to provide theoretical support for nursing managers to prevent and improve emotional suppression in breast cancer patients, and to improve the quality of life of breast cancer patients.Methods: A convenience sampling method was used to select 143 breast cancer patients admitted and hospitalized in the oncology departments of three tertiary hospitals in Luoyang City, Henan Province, China, from November 2023 to January 2024 as the study subjects. A general information questionnaire, an emotional suppression scale, and a family support care index questionnaire were used to conduct the survey.Results: The total score of emotional suppression in breast cancer patients was (32.73 ± 6.59). The results of multiple regression showed that education, disease stage, pain level, and family support were the main influencing factors of emotional inhibition in breast cancer patients (all p < .05).Discussion: Emotional suppression of breast cancer patients in Henan Province is at a medium level; Health care workers and family members should pay more attention to the psychological changes of patients, implement effective nursing interventions, provide positive psychological support, and reduce the psychological burden of patients, to significantly reduce the degree of emotional suppression of patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".