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Record W3047059177 · doi:10.5430/ijhe.v9n5p283

The Anatomy of Persistence: Remediation and Science Identity Perceptions in Undergraduate Anatomy and Physiology

2020· article· en· W3047059177 on OpenAlexvenueno aff
Emily A. Royse, Elliot Sutton, Melanie Peffer, Emily A. Holt

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Health sciencePerceptionPsychologyMedical educationPersistence (discontinuity)Gateway (web page)Mathematics educationMedicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

Undergraduate Anatomy and Physiology (A&P) courses are gateway courses nursing and allied health students must pass before progressing through their academic programs. Many students need to retake the course to receive grades acceptable to progress in their programs, but identifying students at risk of failure may help instructors extend support. In this study, we examined self-efficacy and science identity as potential predictors of student success in these courses, and, by extension, a potential way to identify students at risk of failing. We found that science identity, and not self-efficacy nor completion of science prerequisite courses, explained the most variance when predicting A&P final grade in hierarchical regression. Additionally, we interviewed a purposive sample of students retaking the course to explore their experiences and perceptions of these constructs in A&P over multiple enrollments. Students retaking the course described their experiences of being “biology people” in their interviews, further suggesting that having a science identity is relevant to A&P students and may be leveraged to support students in A&P contexts.

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)0.000
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 score0.187
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.353
Teacher spread0.328 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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