Barriers to Conducting Cancer Trials in CanadA: An Analysis of Key Informant Interviews
Bibliographic record
Abstract
Background: In Canada, there is growing evidence that oncology clinical trials units (ctus) and programs face serious financial challenges. Investment in cancer research in Canada has declined almost 20% in the 5 years since its peak in 2011, and the costs of conducting leading-edge trials are rising. Clinical trials units must therefore be strategic about which studies they open. We interviewed Canadian health care professionals responsible for running cancer trials programs to identify the barriers to sustainability that they face. Methods: One-on-one telephone interviews were conducted with clinicians and clinical research professionals at oncology ctus in Canada. We asked for their perspectives about the barriers to conducting trials at their institutions, in their provinces, and nationwide. Interviews were digitally recorded, transcribed, anonymized, and coded in the NVivo software application (version 11: QSR International, Melbourne, Australia). The initial coding structure was informed by the interview script, with new concepts drawn out and coded during analysis, using a constant comparative approach. Results: Between June 2017 and November 2018, 25 interviews were conducted. Key barriers that participants identified were■ insufficient stable funding to support trials infrastructure and retain staff;■ the need to adopt strict cost-recovery policies, leading to fewer academic trials in portfolios; and■ an overreliance on industry to fund clinical research in Canada. Conclusions: Funding uncertainties have led ctus to increasingly rely on industry sponsorship and more stringent feasibility thresholds to remain solvent. Retaining skilled trials staff can create efficiencies in opening and running studies, with spillover effects of more trials being open to patients. More academic studies are needed to curb industry's influence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
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 | 0.040 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".