Employment status among low-income Caucasian and Latina breast cancer survivors
Bibliographic record
Abstract
6612 Background: Return to work (RTW) after breast cancer is associated with treatment recovery and quality of life. Prior research has found an 80% RTW rate in primarily Caucasian breast cancer survivors; however, little is known about the trajectory of RTW among Latinas. Qualitative research suggests that RTW is a major concern for Latinas. This study compares the rate of RTW between Latinas and Caucasians and investigates the role of job type in RTW. Methods: This is a prospective, longitudinal study of low-income, underserved breast cancer survivors who spoke English or Spanish, did not have metastatic disease, and were enrolled in the Medi-Cal Breast and Cervical Cancer Treatment Program. We interviewed survivors at 6 mos., 18 mos., and 3 yrs. after diagnosis to assess changes in employment status. The impact of independent variables including ethnicity, employment at diagnosis, job type, age, health status, and education was assessed using chi-square tests. Results: 666 survivors completed surveys at both 6 mos. and 3 yrs; 65% were Latina. The median age was 49 and 54 yrs. for Latinas and Caucasians, respectively (p < 0.001). 45% of Latinas had less than a high-school education compared to 3% of Caucasians (p < 0.001). The majority of Latinas worked in 3 job types: personal care-provider (23%), housekeeper (22%), and manufacturing (13%). Caucasians had greater job diversity, including clerical (15%), personal care-provider (13%), food preparer/server (12%), and sales (10%). At diagnosis, 51% of Latinas and 59% of Caucasians were employed (p = 0.07), and among these, Latinas were less likely to be working at 6 and 18 mos. than Caucasians (27% vs. 47% at 6 mos., p = 0.002 and 45% vs. 59% at 18 mos., p = 0.026). This difference dissipated by yr. 3 (53% of Latinas vs. 58% of Caucasians, p = 0.41). Job type at diagnosis was associated with RTW. Conclusions: Employed low-income Latinas and Caucasians appear to follow different RTW trajectories after breast cancer, with fewer Latinas working at 6 and 18 mos. Differences exist in job type between these populations; Caucasians have greater variation in job type and a trend toward greater likelihood of changing job type after breast cancer. This may reflect limitations in career choice among low-income Latinas and may be related to their protracted RTW trajectory. No significant financial relationships to disclose.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".