Use of Gamified Digital Tools in Daily Tasks of Health Care Workers: Scoping Review
Bibliographic record
Abstract
Background: The quality and effectiveness of health care service delivery are significantly influenced by the engagement and motivation of health care workers. Integrating gamified digital tools (GDTs) into health care workers' workflows presents a promising approach to enhancing them. However, there is currently a lack of evidence supporting the implementation of such interventions. Objective: This scoping review aims to summarize existing evidence on the influence of GDTs on the daily tasks of health care workers. Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews. We conducted a comprehensive search across different databases (PubMed, EMBASE, International Journal of Serious Games, Cochrane Library, and Google Scholar) for peer-reviewed studies and (OpenAlex, GreyNet, and IEEE Xplore) for gray literature published between January 2010 and January 2024. Eligibility criteria, developed using the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research) framework, included qualitative, quantitative, and mixed methods studies involving health care workers using GDTs for daily tasks. Studies in English, French, Spanish, or Italian were eligible. Keywords and medical subject headings related to gamification and health care workers were used. The studies were screened, eligibility was assessed, and data were extracted. A narrative synthesis was used to summarize and interpret the findings. Results: Of 5844 studies, 12 met the inclusion criteria and were included in the analysis. These studies exhibited considerable heterogeneity in the application of gamification. Feedback, competition, and dashboard features were the most common gamification elements identified. The implementation of these elements led to enhanced engagement, increased motivation, improved task completion, and promoted healthy competition among staff across various health care settings. Conclusions: Integrating GDTs into health care workers' tasks holds significant potential to enhance engagement and motivation. However, empirical evidence is still lacking. Comparative studies are needed to gain comprehensive insights into the benefits and limitations of gamification in health care.
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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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".