Rural and Remote Registered Nurses' Perception of Working Beyond Their Legislated Scope of Practice
Bibliographic record
Abstract
Registered nurses (RNs) enact their scope of practice in everyday practice through the influences of client needs, the practice setting, employer requirements and policies and the nurse's own level of competence (Canadian Nurses Association 2015). A scope of practice is "dynamic and responsive to changing health needs, knowledge development and technological advances" (International Council of Nurses 2013). In Canada, RNs' scope of practice is set out through provincial and territorial legislation and provincial regulatory frameworks, which are broadly consistent, but vary across provinces (Schiller 2015). Provincial and territorial regulatory bodies articulate the RN scope through frameworks that include expected standards as well as, in some jurisdictions, limits and conditions upon practice (British Columbia College of Nursing Professionals 2018), and which are commonly referred to as a licensed or registered scope of practice. Rural and remote practice is starting to be explicitly acknowledged within nurses' legislated scopes of practice through the identification of certified practices for RNs in specific rural and remote practice settings, following approved education (British Columbia College of Nursing Professionals 2018).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".