The impact of a micro-learning video on the critical appraisal self-efficacy of evidence-based research
Bibliographic record
Abstract
• Little is known about rapid critical appraisal self-efficacy in undergraduate student nurses and the use of micro-learning. • Evaluation of the impact of an educational intervention on rapid critical appraisal of research on undergraduate BSN student nurses’ critical appraisal self-efficacy. • Increasing students’ confidence in their ability to identify valid and trustworthy evidence through the appraisal of clinical research literature motivates student nurses’ behavior to integrate evidence into their practice. National healthcare decisions are being founded on an overabundance of nonpeer-reviewed data flooding the internet. Nurses must develop the skills to identify valid and trustworthy research for practice implementation. The purpose of this study was to evaluate the impact of an educational intervention on rapid critical appraisal of research on undergraduate BSN student nurses’ critical appraisal self-efficacy (CASE) scores. A quantitative, quasi-experimental design was utilized. A convenience sampling of undergraduate BSN nursing students enrolled in a local mid-sized university in British Columbia, Canada. The study used an online survey questionnaire called the New General Self-Efficacy Scale (NGSE) to measure student nurses’ self-efficacy to quickly critique clinical research literature. Forty-six (n=46) students participated in the study. It demonstrated that a generationally appealing five-minute educational video can increase undergraduate student nurses’ CASE. Nursing academia is encountering a new generation of undergraduate nursing students never before experienced – iGen (Gen Z). As such, educational methods for teaching the most important step of evidence-based practice (EBP), critical appraisal, requires further research and consideration.
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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 | 0.029 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".