Medical oncologists’ perceptions of the effect of drug funding decisions for new cancer drugs on their practice: A qualitative study
Bibliographic record
Abstract
6087 Background: As the costs of new cancer drugs rise, drug funding policies for these drugs may have a greater impact on physician practice. Cancer Care Ontario has established a process to “provide equal access to new effective agents for eligible patients throughout the province” through a “New Drug Funding Program” (NDFP). Purpose: The purpose of this study is to describe oncologists’ perceptions of the impact of NDFP drug funding decisions on their practice. Methods: This was a modified ethnographic study involving semi-structured, in-depth interviews with 46 medical oncologists in Ontario from a variety of practice settings. Oncologists were asked to describe the impact of priority setting decisions for new cancer drugs on the care they provided for their patients and their discussions with patients about treatment options. A modified thematic analysis of interview transcripts commenced with data collection. Results: 5 key themes were identified: 1.) Access to Medications: There was concern about accessing effective cancer drugs because of inflexible funding guidelines and inadequate coverage. 2.) Limits Not Accepted: When the NDFP limited access to effective medications, participants generally overcame those limits but at the expense of significant “hassle”, stress and time. 3.) Impact on Physician-Patient Relationship: Drug funding decisions had a significant impact on the time spent on discussions with patients and moral distress regarding the content of discussions. 4.) Disengagement from the Funding Processes: Oncologists did not feel engaged in the drug funding process because of the “hassle” factor, perceived inflexibility of guidelines and the lack of an appeals mechanism. 5.) Quality of Care: Participants did not perceive the NDFP as affecting overall quality of care. Conclusions: Drug funding decisions have a significant impact on oncologists’ practice including their interaction with patients and how they use their time and energies. Policy makers need to consider the implications of physicians who will go to considerable effort to circumvent their policies in the name of patient advocacy. No significant financial relationships to disclose.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".