Measuring Registered Nurse’s Scope of Practice in Primary Care: A Scoping Review of Available Self-Reported Questionnaires
Bibliographic record
Abstract
Many authors have reported significant variability in the scope of practice of registered nurses (RNs) working in primary care clinics. Existing self-reported questionnaires (SRQs) for evaluating nurses' scope of practice in these settings are poorly documented, and the conditions for using SRQs in primary care settings are not well understood. We conducted a scoping review using the Joanna Briggs Institute methodology and Preferred Reporting Items Systematic reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR) guidelines to identify, describe and map current knowledge on SRQs assessing the scope of practice of RNs in primary care. We followed a structured process including search strategy, data extraction and result presentation. This paper presents the results of a scoping review of 12 articles on SRQs assessing nurses' scope of practice in primary care, detailing SRQs, their dimensions, conditions of use and development quality. These results support the need to measure primary care nurses' scope of practice in order to identify the needs and assess the effects of existing and future trainings and organizational structures.
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 | 0.077 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".