Improving attitudes toward poverty and attitudes toward interprofessional collaboration through online interprofessional synchronous poverty simulation: A mixed methods comparison study
Bibliographic record
Abstract
Introduction: Interprofessional poverty simulations can improve attitudes toward poverty and attitudes toward interprofessional collaboration. This study evaluated an immersive synchronous online poverty simulation.Methods: A mixed-method study was conducted to compare the outcomes of onsite and online interprofessional poverty simulations. The simulations were carried out at a private university in the US 6 times onsite between 2017 and 2019, and 4 times online between 2020 and 2021. The quantitative portion utilized two pre- and post-test questionnaires: the Attitudes Toward Poverty Short Form and the University of West England Interprofessional Questionnaire, which evaluate attitudes towards poverty and interprofessional collaboration respectively. Additionally, qualitative interviews of selected students were conducted 2-4 weeks after the simulations. Quantitative data were analyzed using paired t-tests for individual results, and independent samples t-tests to compare onsite with online pre-post changes. Qualitative data were evaluated using thematic analysis by faculty members from three disciplines.Results: The research indicates that both online and onsite poverty simulations can improve student attitudes toward both poverty and interprofessional collaboration. Results for 196 online participants were compared to 325 onsite participants. Both online and onsite groups showed significant improvements in attitudes toward poverty and interprofessional collaboration (p < .05). The quantitative effect size was smaller for online than onsite, but the difference was less in 2021, the second year of the online simulation, likely due to improved implementation techniques. The qualitative data suggested a less intense emotional response for online participants compared to onsite. Overall results suggest that there is a learning curve in offering an effective online poverty simulation, but that online poverty simulations do significantly influence attitudes toward poverty and interprofessional collaboration.Recommendation: In the article, lessons learned are shared. Online simulations can effectively change attitudes toward poverty, and allow many students to participate who otherwise might not be able to, but the magnitude of the impact for our population was not as great online as onsite. It is recommended that schools of nursing and faculty of other healthcare professions consider the pros and cons of incorporating interprofessional poverty simulations in their curricula.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".