Gender Socialization as a Predictor of Psychosocial Well-Being in Young Women with Breast Cancer
Bibliographic record
Abstract
This study aimed to explore the relationship between gender socialization and psychosocial well-being among young women diagnosed with and treated for breast cancer. A total of 113 women between the ages of 18–49 completed a one-time questionnaire package. Four key measures of gender socialization were included: Gender Role Socialization Scale (GRSS), Objectified Body Consciousness Scale (OBCS), Mental Freedom Scale (MFS), and Silencing the Self Scale (SSS). Two measures of psychosocial well-being were included: Functional Assessment of Cancer Therapy-Breast (FACT-B) and Experience of Embodiment Scale (EES). Correlational and regression analyses were conducted to assess the relationship between gender socialization variables and well-being. In multiple regression models, GRSS and MFS added significant increments to the prediction of variance of the FACT-B (R2 = 23.0%). In contrast, the OBCS and MFS added significant increments to the prediction of variance of the EES (R2 = 47.0%). Findings suggested that women with greater endorsements to proscribed gender socialization were associated with poor well-being scores. Women who endorsed a critical stance, resisting traditional gender-role expectations, objectification pressures, and other social discourses, were associated with greater well-being scores. Future studies are needed to examine the impact of gender socialization on the well-being of young people with breast cancer.
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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) | 0.001 | 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".