Academic Integrity in a Student Practice Environment--An Elicitation Study
Bibliographic record
Abstract
Higher education offers an environment to acculturate students to the values of academic integrity (AI) [honesty, trust, fairness, respect, responsibility, courage] that align with the core values of most professions. Many professional programs include a practice or service component to the educational experience offering students the opportunity to embody and practice these values in workplace settings. Using the Theory of Planned Behaviour as a theoretical framework, students’ common attitudinal, subjective norms, and perceived behavioural controls in adopting AI values in their practice environments was the focus of an Elicitation Study. Thirty senior nursing students reported three major concepts related to their experiences with AI in clinical learning settings: Effects on Professionals, Effects on Students, and Effects on Patients. Respondents described the categories of helpful relationships, respect and trust, benefits and losses, patient safety and care that are connected and contingent on their ability to practice with AI. Thispaper describes the findings from the Elicitation Study that helped inform the creation of the Miron Academic Integrity Nursing Survey (MAINS) used in a large doctoral study.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".