Physician experience and challenges obtaining unfunded oral chemotherapy across Canada.
Bibliographic record
Abstract
e16583 Background: Previous studies have shown hematologists and medical oncologists may not accept the financial limits set by governing agencies on access to oral chemotherapy for their patients. This requires increased time and effort on the clinician’s part to obtain funding. We captured physicians’ perceptions of barriers to obtaining unfunded chemotherapies and methods used to overcome these barriers. Methods: A total of 640 medical oncologists and hematologists were surveyed using a web-based survey tool. A thirteen-item survey was designed to assess the practice type along with time spent and methods used on obtaining unfunded oral chemotherapy. Results: Of the 640 invitees, 568 were delivered and 168 responded,(response rate 30%). 91 respondents were medical oncologists, 44 were hematologists and 33 treated both solid and non-solid malignancies. 65% of physicians spent an average of 1-4 hours obtaining funding for oral drugs for patients per week. 62% indicated their institution has a drug access coordinator (DAC). Having a DAC did not impact the proportion of physicians spending >4 hrs/ week accessing oral drugs (38% vs 29%, p=NS). To overcome barriers to funding, physicians’ enrolled patients on clinical trials (91%), used compassionate access programs (96%) or special request forms to government (92%). Other methods included writing false claims on forms to fit funding criteria for a drug (36%) or use of leftover drug supplies (36%). The majority of physicians felt that their inability to obtain unfunded medications for their patients has negatively impacted patients’ clinical outcomes (56%) and psychosocial quality of life (74%). Of all respondents, only 28% of physicians contacted their governing body with concerns of funding for oral chemotherapy. Conclusions: Practicing hematologists and medical oncologists in Canada use numerous methods to obtain unfunded oral chemotherapies, including lying on access forms. Overall, the majority of physicians spend 1-4 hours a week on obtaining funding; this did not differ for physicians with or without a DAC at their practice. Despite the challenges in accessing oral chemotherapies, most physicians have not contacted governing bodies for legislative change.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".