Satisfaction with Care among Low-Income Women with Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is an important outcome measure in determining quality of care. There are few data evaluating patient satisfaction in nonwhite, low-income populations. The objective of this study was to identify the structure, process, and outcome factors that impact patient satisfaction with care in a low-income population of women with breast cancer. METHODS: In a cross-sectional survey of low-income women newly diagnosed with breast cancer, eligible women enrolled in the California Breast and Cervical Cancer Treatment Program (BCCTP) from February 2003 through September 2005 were interviewed by phone 6 months after their enrollment. This was a population-based sample of women aged >or=18 years (n = 924) with a definitive diagnosis of breast cancer and enrolled in the BCCTP. The main outcome measure was satisfaction with care received. RESULTS: Random effects logistic regression revealed that less acculturated Latinas were more likely (odds ratio, [OR] = 5.36, p < 0.000) to be extremely satisfied with their care compared with non-Hispanic white women. Women who believed they could have been diagnosed sooner were less likely to be extremely satisfied (OR = 0.61, p < 0.000). Women who had received or were receiving radiotherapy or chemotherapy had nearly twice the odds of being extremely satisfied (OR = 2.02, p < 0.000, and OR = 2.13, p < 0.000, respectively). Greater information giving was associated with greater satisfaction (OR = 1.17, p < 0.000). Women reporting greater physician emotional support were more likely to report being extremely satisfied (OR = 1.26, p < 0.000). A higher participatory treatment decision-making score was associated with greater satisfaction (OR = 1.78, p < 0.000). CONCLUSIONS: In a low-income population, satisfaction is also reported at high levels. In addition to age, ethnicity/acculturation, receipt of chemotherapy and radiotherapy, physician emotional support, and collaborative decision making, perception of diagnostic delay is a predictor of dissatisfaction in this population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".