The impact of regulatory perspectives and practices on professional innovation in nursing
Bibliographic record
Abstract
Since at least the 1970s in Canada, there have been calls for health system reforms based on innovative roles and expanded scopes of practice for nurses. Professional regulatory organizations, through legislation, define the standards and parameters of professional nursing practice. Nursing regulators emphasize public protection over the advancement of nursing; regulatory processes and decisions tend to be conservative and risk-averse. This study explored the impact that regulatory processes have on innovation in nursing roles. Nurses in a range of unique practice situations were interviewed, including nurses in non-traditional roles and/or settings, those with cross-jurisdictional career histories, and those working in interdisciplinary practices and educational settings. For these nurses, nursing practice was viewed through a traditional clinical lens, which did not fit for them. They experienced hassle, delay, and inconsistencies in regulatory practices. They felt mistreated and fearful of the regulator and lamented the ways in which ambitious, creative, capable nurses were stymied in attempting new applications for nursing knowledge. Nursing is constraining its own mandate to contribute to health care through stringent licensing processes. Healthcare reform provides an opportunity for nursing regulators to rethink their processes and provide the latitude for nurse-driven change.
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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 | 0.094 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.079 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| 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, 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".