Allied health, nursing and midwifery expert (consultant) practitioner learning and development: protocol for an international scoping review
Bibliographic record
Abstract
Background: Growing complexity in healthcare is a direct consequence of ageing and higher prevalence of non-communicable diseases such as musculoskeletal disorders, cardiovascular disease and cancer. This requires transformation of the healthcare workforce by developing learning and development opportunities that will attract and retain staff. Expert practice roles have been developed to provide this leadership across allied health, nursing and midwifery internationally. However, the term expert is yet to be defined. An international scoping review is required to identify and synthesise existing literature to clarify the definitions of expert practice, explore learning and development pathways leading to, and to sustain, this level of practice. Methods: This scoping review protocol will be informed by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) extension for scoping reviews. EMBASE, SCOPUS, Web of Science, Google Scholar and Grey Literature will be rigorously searched. Two independent reviewers will conduct the searches, assess for eligibility and undertake a quality assessment. A 3-phase analysis will follow to interrogate the data; phase 1 and 2 will be content analysis, and phase 3 will be thematic analysis. Ethics and dissemination: No personal, confidential or primary data will be collected so ethical approval is not required. The results of this review will be shared by publication in a journal.
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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 | 0.143 | 0.146 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.024 |
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".