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Record W2050909553 · doi:10.3138/ptc.2011-13

Academic Dishonesty among Physical Therapy Students: A Descriptive Study

2012· article· en· W2050909553 on OpenAlexaffvenueabout
Eli Montuno, Alex Davidson, Karen Iwasaki, Susan Jones, Jay Martin, Dina Brooks, Barbara E. Gibson, Brenda Mori

Bibliographic record

VenuePhysiotherapy Canada · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeriousnessCheatingAcademic dishonestyMedicinePsychologyMedical educationSocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: To examine academically dishonest behaviours based on physical therapy (PT) students' current practices and educators' prior behaviours as PT students. METHOD: A Web-based questionnaire was sent to 174 students and 250 educators from the PT programme at the University of Toronto. The questionnaire gathered data on demographics as well as on the prevalence of, seriousness of, and contributing factors to academic dishonesty (AD). RESULTS: In all, 52.4% of educators and 44.3% of students responded to the questionnaire over a 6-week data-collection period. Scenarios rated the most serious were the least frequently performed by educators and students. The impact of generation on attitudes and prevalence of AD was not significant. The factors most commonly reported as contributing to AD were school-related pressure, disagreement with evaluation methods, and the perception that "everyone else does it." CONCLUSION: This study parallels the findings of similar research conducted in other health care programmes: AD does occur within the PT curriculum. AD was more prevalent in situations associated with helping peers than in those associated with personal gain. The consistency in behaviours reported across generations suggests that some forms of cheating are accepted as the social norm and may be a function of the environment.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.351
Teacher spread0.326 · 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

Citations26
Published2012
Admission routes3
Has abstractyes

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