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Record W2004724195 · doi:10.1145/1328202.1328248

Interactive community simulation environment for community health nursing

2007· article· en· W2004724195 on OpenAlexaff
Michelle Hogan, Hamed Sabri, Bill Kapralos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCurriculumNurse educationNursingCommunity healthProcess (computing)Instructional simulationVirtual communityNursing processMedicineMedical educationPsychologyComputer scienceEducational technologyPedagogyPublic health

Abstract

fetched live from OpenAlex

The majority of nursing curriculums continue to relate experiences and examples of nursing to the more familiar role of "nurse clinician". Specifically, the use of simulation and technology has been used in the undergraduate nursing program to assist learners in developing nursing skills and knowledge for treating individual patients with acute and chronic conditions. Nursing students are now able to apply learned concepts of nurse clinician when treating virtual patients and while engaging in simulation-based education. The use of such simulation in undergraduate nursing education allows learners to readily apply skills and knowledge within a safe learning environment; however, the use of such technology has not been widely adopted to address the learning needs of today's community health nursing students. In fact, despite its importance, the role and process of community health nursing is often unknown to many undergraduate nursing students. This paper presents a strategy-based, interactive community simulation environment that addresses the learning needs of millennial students within a community health nursing curriculum.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.461
Teacher spread0.359 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2007
Admission routes1
Has abstractyes

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