Incorporating lead education content into undergraduate nursing curriculum: Impact on knowledge and confidence
Bibliographic record
Abstract
Background and objective: Lead poisoning is a major public health crisis in Michigan. The purpose of this study was to explore the impact of an education intervention on knowledge and confidence levels among nursing students enrolled in the pre-licensure Bachelor of Science in Nursing and Registered Nurse to Bachelor of Science in Nursing (RN2BSN) program.Methods: The study used a quantitative pre- and post-test design to assess the impact of lead health learning activities on knowledge and confidence among undergraduate nursing students in the Midwestern United States. The final study sample included 115 nursing students from two student cohorts. The study instrument used 26-item Nursing Students Lead Knowledge and Confidence Scale; independent sample t-tests, paired sample t-test and Cohen’s d for the effect size were used in data analyses.Results: The education improved total knowledge and confidence on both groups whereas RN2BSN students had larger effect sizes on the differences of pre- to post-test scores than pre-licensure students in general lead knowledge, lead exposure knowledge, total lead knowledge, and confidence.Conclusions: The results contribute to limited literature examining a critical public health concern regarding lead health exposure and prevention education of nursing students. Incorporating such content area into nursing curriculums is essential in ensuring that such public health disparities are mitigated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".