Gamification in nursing literature: an integrative review
Bibliographic record
Abstract
Abstract Objective Gamification is an increasingly popular instructional strategy in nursing. The purpose of this integrative review is to explore gamification as it has been applied in nursing literature. This integrative review seeks to ask the question – What aspects of gamification have been explored in nursing literature and what aspects require further exploration? Method Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Methodological Issues in Nursing Research , 52(5), 546–553 integrative review framework guided this review. Seventeen articles were reviewed and a quality appraisal tool (developed by Hawker, S., Payne, S., Kerr, C., Hardey, M., & Powell, J. (2002). Appraising the evidence: Reviewing disparate data systematically. Qualitative Health Research , 12(9), 1284–1299) was also used to evaluate the articles. Results Following the data analysis stage outlined in Whittemore and Knafl’s integrative review framework, six themes emerged: construct conceptualization; relationship between engagement, satisfaction, and knowledge retention; knowledge translation, motivation, role of technology, and gamification elements. Conclusion Gamification is of interest to the nursing profession. More study is needed to better ascertain the relationship between gamification and several of the main themes identified in this review.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".