Developing Canada's clinical research capacity and relevance: Change management issues
Bibliographic record
Abstract
Introduction: Despite significant investments in clinical research over the years, the pandemic illustrated Canada's limited ability to rapidly deploy research programs that could have generated immediately useful policy and health care information. This qualitative study aimed to identify the barriers, facilitators, and possible solutions to strengthen Canada's practice-changing clinical research enterprise (or ecosystem). Methods: Through purposive sampling, stakeholders representing various perspectives on Canada's clinical research ecosystem engaged in semi-structured interviews. We continued to gather stakeholders until reaching data sufficiency. The collected information was coded and analyzed using QDA Miner based on Lewin's force field framework. Results: Thirteen participants identified four main driving forces—dynamic scientific community, involvement of patients and clinicians, innovation capacity, and research champions—and six barriers—legal, ethical, data access, administrative inefficiencies, protectionism and regionalism, and lack of incentives—that they believe explain the current state of clinical research across the country. In light of these barriers and facilitators, they proposed six strategies to fully realize Canada's clinical research potential: nationalization of the research infrastructure, prioritization of the portfolio, enhancement of representativeness, investment in health and scientific literacy, promotion of a cultural shift, and development of human resources for research. The proposed solutions require a degree of consensus and enhanced coordination among different levels of government. Discussion: Given the numerous barriers and perceived constraints, meaningful changes to facilitate the implementation of practice-changing research within a learning health system in Canada will require both policy and cultural changes, which will also necessitate broad stakeholder support.
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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 | 0.086 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.032 | 0.028 |
| Scholarly communication | 0.032 | 0.008 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".