Education programmes on performance-based assessment for allied health and nursing clinical educators: A scoping review protocol [version 3; peer review: 1 approved, 2 approved with reservations]
Bibliographic record
Abstract
Background Performance-based assessment (PBA) is a complex process undertaken in the workplace by healthcare practitioners known as clinical educators, who assist universities in determining health professional students’ readiness for independent practice. Preparing healthcare professionals for PBA is considered essential to ensuring the quality of the assessment process in the clinical learning environment. A preliminary search of the literature indicated a paucity of research guiding the development of education programmes that support practice educators to understand and implement PBA. Objective The aim of this scoping review is to investigate and describe education programmes delivered to allied health and nursing clinical educators, to develop PBA knowledge and skills. Methods This review will follow the Joanna Briggs Institute (JBI) methodology for conducting scoping reviews. Electronic databases relevant to this research topic will be searched including, EMBASE, ERIC, MEDLINE (Ovid), Web of Science and CINAHL and other targeted databases for grey literature. Studies that include PBA as the main focus or a component of the education programmes, of any format, delivered to clinical educators in allied health and nursing will be included. Studies may report the design and/or implementation and/or evaluation of PBA education programmes. Relevant English language publications will be sought from January 2000 to October 2022. Two reviewers will screen all titles and abstracts against the inclusion/exclusion criteria, and publications deemed relevant will be eligible for full text screening, confirming appropriateness for inclusion in the scoping review. Data will be charted to create a table of the results, supported a by narrative summary of the findings in line with the review objectives.
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 | 0.180 | 0.160 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.020 |
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".