Assessment of clinical skills in laboratory settings in prelicensure health education programs: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to map the available literature on the assessment of clinical skills in laboratory settings in health education prelicensure programs. INTRODUCTION: Prelicensure health programs are facing growing challenges in delivering learning experiences that sufficiently prepare students for safe practice as clinical settings contend with workforce shortages, rising workload demands, and increasingly complex patient populations. One safety component is the assessment of clinical skills prior to entering practice and caring for patients. Laboratory experiences generally include opportunities for hands-on practice and demonstration of a new clinical skill, such as medication administration or infection prevention and control measures. This scoping review is necessary as laboratory assessments play a crucial role in providing insights into students' readiness to perform relevant clinical skills prior to caring for patients in practice settings. ELIGIBILITY CRITERIA: This review will consider qualitative, quantitative, and mixed methods studies on approaches and strategies used by faculty, staff, and/or students when assessing clinical skills in laboratory settings in health education prelicensure programs. Assessment of postlicensure health care professionals' clinical skills will be excluded. METHODS: This review will follow the JBI methodology for scoping reviews. Databases to be searched will include CINAHL with Full Text (EBSCOhost), MEDLINE (Ovid), Embase (Embase.com), ERIC (EBSCOhost), ProQuest Dissertations and Theses (ProQuest), and Google (with advanced search strategies). Two independent reviewers will screen citations for inclusion as well as conduct data extraction and analysis. A third reviewer will resolve any disagreements. Data will be presented in tables and charts, accompanied by a narrative summary. REVIEW REGISTRATION: OSF https://osf.io/yz5bu.
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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 | 0.118 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".