A bibliographic exploration of nursing's scope of practice
Bibliographic record
Abstract
AIM: To conduct a bibliographic analysis of the indexed literature relating to scope of practice in nursing so as to identify underlying patterns in journal publication, volume of scholarly work over time, countries of origin, central contributors, academic affiliation and the major dimension of the studies conducted. METHODS: A systematic search of the Scopus database provided data that was then extracted and utilized to undertake a bibliometric analysis of published work relating to scope of practice. In addition to identification of aggregated metrics relating to the most frequently occurring journals and most cited authors, a co-word analysis was conducted. RESULTS: A total of 2730 articles with the term Scope of Practice in the Title, Abstract or Keywords were identified. Co-word analysis revealed five major themes - Changing Regulatory Environment; Health Care Drivers; Competence & Role Implementation; Policy Context; and Role Evolution & Role Differentiation. CONCLUSIONS AND POLICY IMPLICATIONS: From a policy perspective, we conclude that bibliographic analysis of the indexed literature is a useful technique that can augment our understanding of key regulatory issues such as scope of practice. However, the overemphasis on advanced practice in the scope of practice literature coupled with the increased interest in task shifting to support-personnel as governments pursue the goal of universal health coverage may leave nursing inadequately prepared to inform any evidence-based policy change.
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 | 0.023 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.147 | 0.185 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".