Developing a pan-Canadian nursing regulation research agenda
Bibliographic record
Abstract
Effective nursing regulation is vital for maintaining a competent, agile, and safe nursing workforce. Yet, the evidence available to inform nursing regulation within the Canadian context is largely descriptive and fragmented, with limited utility to support decision-making. While nursing regulators, which exist at the provincial and territorial levels, continue to engage in pan-Canadian policy initiatives, no coordinated research agenda exists to drive nursing regulatory science forward. To address this gap, we conducted a virtual deliberative dialogue with diverse system partners across Canada to co-create a pan-Canadian nursing regulation research agenda. Examples of research priority themes identified by the participants include strategies for regulatory harmonization, evaluation of regulatory reforms, licensure policy development, improved regulatory data management, and regulatory approaches for new and emerging practices. Barriers to collaboration centered on jurisdictional differences in legislative frameworks and priorities, lack of resources and time, poor role clarity, and legislative barriers to data collection. Examples of facilitators included leveraging existing collaborative networks, addressing barriers to data sharing, and enhancing partnerships between regulators and researchers. Guided by the learning health system framework, we explore strategic opportunities to create a "learning regulatory system" by highlighting scientific, social, technological, policy, legal, and ethical considerations. Insights from our dialogue reinforce the need for intentional investment in collaborative infrastructure to support continuous improvement and innovation in nursing regulation.
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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 | 0.128 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".