Return to work in low‐income Latina and non‐Latina white breast cancer survivors: A 3‐year longitudinal study
Bibliographic record
Abstract
BACKGROUND: Previous research has found an 80% return-to-work rate in mid-income white breast cancer survivors, but little is known about the employment trajectory of low-income minorities or whites. We set out to compare the trajectories of low-income Latina and non-Latina white survivors and to identify correlates of employment status. METHODS: Participants were low-income women who had localized breast cancer, spoke English or Spanish, and were employed at the time of diagnosis. Interviews were conducted 6, 18, and 36 months after diagnosis. Multivariate logistic regression was used to identify independent correlates of employment status at 18 months. RESULTS: Of 290 participants, 62% were Latina. Latinas were less likely than non-Latina whites to be working 6 months (27% vs 49%; P = .0002) and 18 months (45% vs 59%; P = .02) after diagnosis, but at 36 months there was no significant difference (53% vs 59%; P = .29). Latinas were more likely to be manual laborers than were non-Latina whites (P < .0001). Baseline job type and receipt of axillary node dissection were associated with employment status among Latinas but not non-Latina whites. CONCLUSIONS: Neither low-income Latinas nor non-Latina whites approached the 80% rate of return to work seen in wealthier white populations. Latinas followed a protracted return-to-work trajectory compared to non-Latina whites, and differences in job type appear to have played an important role. Manual laborers may be disproportionately impacted by surgical procedures that limit physical activity. This can inform the development of rehabilitative interventions and may have important implications for the surgical and postsurgical management of patients.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".