The effect of priority setting decisions for new cancer drugs on medical oncologists' practice in Ontario: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Health care policies, including drug-funding policies, influence physician practice. Funding policies are especially important in the area of cancer care since cancer is a leading cause of death that is responsible for a significant level of health care expenditures. Recognizing the rising cost of cancer therapies, Cancer Care Ontario (CCO) established a funding process to provide access to new, effective agents through a "New Drug Funding Program" (NDFP). The purpose of this study is to describe oncologists' perceptions of the impact of NDFP priority setting decisions on their practice. METHODS: This is a qualitative study involving semi-structured, in-depth interviews with 46 medical oncologists in Ontario. Oncologists were asked to describe the impact of CCO's NDFP drug funding decisions on their practice. Analysis of interview transcripts commenced with data collection. RESULTS: Our key finding is that many of the medical oncologists who participated in this study did not accept limits when policy decisions limit access to cancer drugs they feel would benefit their patients. Moreover, overcoming those limits had a significant impact on oncologists' practice in terms of how they spend their time and energy and their relationship with patients. CONCLUSION: When priority setting decisions limit access to cancer medications, many oncologists' efforts to overcome those limits have a significant impact on their practice. Policy makers need to seriously consider the implications of their decisions on physicians, who may go to considerable effort to circumvent their policies in the name of patient advocacy.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".