Overcoming Obstacles in Accessing Unfunded Oral Chemotherapy: Physician Experience and Challenges
Bibliographic record
Abstract
PURPOSE: Previous studies have shown hematologists and medical oncologists may not accept the financial limits set by governing agencies on patient access to oral chemotherapy. The purpose of this study was to capture the methods physicians used to overcome barriers to accessing chemotherapeutic regimens for their patients. METHODS: A total of 640 medical oncologists and hematologists across Canada were surveyed using a 13-item Web-based survey tool. The survey was delivered by e-mail with three follow-up reminders. After a response period of 3 months, results were collated and analyzed with descriptive statistics. RESULTS: Of the 640 invitations, 568 were successfully delivered, and 183 responses were received (response rate, 32.0%). Among respondents, 101 treated solid malignancies (55.2%), 49 treated nonsolid malignancies (26.8%), and 33 treated both (18.0%). To overcome funding barriers, participating oncologists enrolled patients onto clinical trials (90.5%), used compassionate access programs (96.1%), and made special requests to government (91.8%). Other methods included writing false claims on forms to fit funding criteria for drugs (31.1%) and using leftover drug supplies (31.0%). Physicians felt their inability to obtain unfunded medications had a negative impact on their patients' clinical outcomes (56.0%) and psychosocial quality of life (73.0%). Only 28.5% of physicians contacted their governing body with concerns about oral chemotherapy funding. CONCLUSION: Canadian physicians use numerous methods to obtain unfunded oral chemotherapies, including falsifying claims on access forms and submitting special requests to government agencies. Further study is warranted to explore the disconnection between policymakers and physicians with regard to funding of oral chemotherapies.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".