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Record W3138796342 · doi:10.1515/ijnes-2020-0081

Gamification in nursing literature: an integrative review

2021· review· en· W3138796342 on OpenAlexaff
Upinder Sarker, Heather Kanuka, Colleen M. Norris, Christy Raymond, Olive Yonge, Sandra Davidson

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2021
Typereview
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsConceptualizationConstruct (python library)Nursing literaturePsychologyNursingMedical educationMedicineAlternative medicineComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.128
GPT teacher head0.546
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Nursing Education ScholarshipSame topicEducational Games and GamificationFrench-language works237,207