Barriers to Breast and Colorectal Cancer Survivorship Care: Perceptions of Primary Care Physicians and Medical Oncologists in the United States
Bibliographic record
Abstract
PURPOSE: High-quality, well-coordinated cancer survivorship care is needed yet barriers remain owing to fragmentation in the United States health care system. This article is a nationwide survey of barriers perceived by primary care physicians (PCPs) and medical oncologists (MOs) regarding breast and colorectal cancer survivorship care beyond 5 years after treatment. METHODS: The Survey of Physician Attitudes Regarding the Care of Cancer Survivors was mailed out in 2009 to a nationally-representative sample (n = 3,596) of US PCPs and MOs. Ten physician-perceived cancer survivorship care barriers/concerns were compared between the two provider types. Using weighted multinomial logistic regression, we modeled each barrier, adjusting for physician demographics, reimbursement, training, and practice characteristics. RESULTS: We received responses from 2,202 physicians (1,072 PCPs; 1,130 MOs; 65.1% cooperation rate). In adjusted patient-related barriers models, MOs were more likely than PCPs to report patient language barriers (odds ratio, [OR], 1.72; 95% CI, 1.22 to 2.42), insurance restrictions impeding test/treatment use (OR, 1.42; 95% CI, 1.03 to 1.96), and patients requesting more aggressive testing (OR, 4.08; 95% CI, 2.73 to 6.10). In adjusted physician-related barriers models, PCPs were more likely to report inadequate training (OR, 3.06; 95% CI, 2.03 to 4.61) and ordering additional tests/treatments because of malpractice concerns (OR, 1.87; 95% CI, 1.20 to 2.93). MOs were more likely to report uncertainty regarding general preventive care responsibility (often/always: OR, 1.97; 95% CI, 1.13 to 3.43; sometimes: OR, 2.16; 95% CI, 1.60 to 2.93). CONCLUSION: MOs and PCPs perceive different cancer follow-up care barriers/concerns to be problematic. Resolving inadequate training, malpractice-driven test ordering, and preventive-care responsibility concerns may require continuing education, explicit guidelines, and survivorship care plans. Reviewing care plans with survivors may also reduce patients' requests for unnecessary testing.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".