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Record W4285992815 · doi:10.1007/s40979-022-00107-y

Academic integrity in upper year nursing students’ work-integrated settings

2022· article· en· W4285992815 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal for Educational Integrity · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsQueen's UniversityHumber Polytechnic
Fundersnot available
KeywordsHonestyAcademic integrityPsychologyWork (physics)Scientific integrityCouragePersonal IntegrityAcademic dishonestyPedagogyMedical educationEngineering ethicsSocial psychologyMedicinePolitical scienceCheating

Abstract

fetched live from OpenAlex

Abstract Work-integrated learning (WIL) is an educational approach that aims to support students’ integration of theory to practice. These rich learning opportunities provide students with real-world experiences and introduce practice and ethical situations that help consolidate and bridge their knowledge and skill. Academic integrity has been defined as the ongoing commitment to values that are consistent with ethical practice: honesty, trust, fairness, respect, responsibility, and courage (International Centre for Academic Integrity, 2021). It is important to understand what specifically influences students’ intentions to behave with integrity in WIL settings. This paper reports on one study that explored predictors to students’ intentions to behave with integrity across three different WIL settings in their upper years of studies. The findings and recommendations from the research may help to inform other professional programs that include WIL through their educational offerings.

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.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.438
Teacher spread0.392 · 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