Rural and Remote Licensed Practical Nurses' Perception of Working Below Their Legislated Scope of Practice
Bibliographic record
Abstract
Over the past two decades in Canada, licensed or registered practical nurses (LPNs) have experienced an extension of their educational preparation and scope of practice. Simultaneously, there has been an increase in the number of LPNs employed in rural and remote communities. These changes have influenced the practice environment and LPNs' perceptions of their work. The aim of this article is to examine what factors predict rural and remote LPNs' perceptions of working below their legislated scope of practice and to explore their perceptions of working below scope. The findings arise from a national survey of rural and remote regulated nurses, in which 77.3% and 17.6% of the LPNs reported their practice as within and as below their legislated scope of practice, respectively. Three factors, age, stage of career and job-resources related to autonomy and control, predicted that LPNs would perceive themselves to be working below their scope of practice. These results suggest that new ways to communicate nurses' scope of practice are needed, along with supports to help rural and remote LPNs more consistently practice to their legislated scope of practice. Without such changes, the LPN role cannot be optimized and disharmony within rural and remote settings may be exacerbated.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".