Tackling teaching patient safety: gamification to the rescue
Bibliographic record
Abstract
Healthcare professionals (HCPs) are committed to providing safe and effective patient care. For HCP learners, teaching and learning patient/medication safety in an in-person, hands-on, clinical environment is ideal. However, the COVID-19 pandemic has pivoted educational delivery: How can we engage students in an online environment? What patient/medication safety topics would learners appreciate more help from their teachers to enhance knowledge retention or reinforcement? We surveyed two cohorts of the University of Toronto pharmacy students enrolled in a patient/medication safety course (PHM322) virtually offered in 2021 and 2022. We received a 14% response rate (n = 10), where students reported the most challenging concepts were prospective and retrospective incident analysis tools—root cause analysis (RCA), failure mode and effects analysis (FMEA) and multi-incident analysis (MIA). We conducted a literature review on gamification in health profession education. A semi-experimental study found that Kahoot (a trivia platform) helped increase knowledge scores of nursing students.1 Although the use of gamification was not extensively studied in pharmacy education, it may be an option for supporting knowledge reinforcement in RCA, FMEA and MIA while engaging students in the online offering of PHM322. We developed Safety Games with brief, trivia-style multiple choice questions (MCQs) and administered them synchronously during class time. The game platform (Quizizz) allows students to participate virtually using their computer or mobile device. We achieved a 100% participation rate. We also disseminated pre- and post-game questionnaires on knowledge assessment of the three topics and student experience. Response rates were 87% (n = 39) and 38% (n = 17), respectively, for the pre- and post-game questionnaires, with a mean knowledge score increase from 42% to 66% (p = 0.0027). Most students enjoyed their participation and would recommend the Safety Games to their peers. They indicated the games helped them 'reflect on how much [they] had retained', 'recall the concepts learned in class', identify 'gaps in [their] knowledge' and 'clarify misunderstandings'. Our project identified the areas where students would benefit from knowledge reinforcement strategies when teaching patient/medication safety in pharmacy education (i.e. RCA, FMEA and MIA) and contributed to the pharmacy education literature regarding the use of gamification as a pedagogical tool in online learning. Our Safety Games were feasible to implement during class time in an online or hybrid environment as they were easily accessible through simple log-in and participation processes. They were also effective in promoting knowledge retention where the first three cognitive levels of remembering, understanding and applying RCA, FMEA and MIA principles, as stated in the revised Bloom's taxonomy, were achieved. Participants also reported positive experiences with the Safety Games, likely due to the increased self-perceived confidence as reflected in their responses and the use of a commercially available game platform that incorporates user experience in its design. Despite a small sample size, our project has fulfilled the first two levels in Kirkpatrick's Four-Level Training Evaluation, that is, reaction—satisfaction and learning—knowledge gain. We presented a successful innovation that could inspire more ventures in gamified pharmacy education. Wei Wei: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; writing—original draft; writing—review and editing. Victoria Ezekwemba: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; writing—original draft; writing—review and editing. Certina Ho: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; writing—original draft; writing—review and editing.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".