Nursing Students Learn Online Interprofessional Education on Substance Use
Bibliographic record
Abstract
Background Interprofessional education strategies are becoming more prevalent as nursing schools integrate interprofessional practice activities into their curricula. Purpose This paper presents the results of a federally funded project to deliver online interprofessional education to nursing students on screening for alcohol and substance use in rural areas, in which their perceptions about interprofessional education were measured. Methods A quasi-experimental within-subjects repeated measures design was utilized. Students in the bachelor or associate degree program were recruited from two rural nursing schools. A demographic questionnaire, Alcohol and Alcohol Problems Questionnaire, Drug and Drug Problems Questionnaire, and Interprofessional Education Perception Scale were utilized. General linear modeling was used to determine changes in these measurements over time. Data collection was performed at pretraining, posttraining, and following an online interprofessional dialogue. Results The study consisted of 89 nursing students. The participants were 87% female (n = 77/89) and 91% white (n = 81/89); their mean age was 24.9 years (standard deviation = 10.36). Analysis of evaluation questionnaires demonstrated increased levels of confidence in working with patients who consume alcohol or other drugs and on certain aspects of interprofessional education. Conclusion Online interprofessional preservice education holds the potential to positively increase nursing students’ confidence in working with patients and to increase their interprofessional practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".