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Record W4245270010 · doi:10.47678/cjhe.v49i1.188220

Sinking or Swimming in an Academic Pool: A Study of Resiliency and Student Success in First-Year Undergraduates

2019· article· en· W4245270010 on OpenAlexaffvenue
Claire A. Wilson, Sarah E. Babcock, Donald H. Saklofske

Bibliographic record

VenueCanadian Journal of Higher Education · 2019
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsWestern University
Fundersnot available
KeywordsFacet (psychology)PsychologyAdaptabilityAcademic achievementPerfectionism (psychology)PersonalitySelf-efficacyEmotional intelligenceMultilevel modelBig Five personality traitsDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The transition from high school to post-secondary education presents challenges for students. Many variables have been identified as significant predictors of student achievement. Resiliency, defined as the ability to overcome challenges and adversity, may be particularly relevant during the adjustment to post-secondary education. This study assesses whether resiliency incrementally predicts student success after controlling for additional predictors. Participants were 277 undergraduate students who completed self-reports of academic skills, resiliency, personality variables, emotional intelligence (EI), and perfectionism. Students’ year-end GPA was collected from the university registrar. Hierarchical regression analysis revealed that resiliency, measured by sense of mastery, negatively predicted GPA after controlling for other predictors. The sense of mastery facet of self-efficacy positively predicted GPA; however, the adaptability facet was a significant negative predictor of GPA. Findings suggest that self-efficacy is a salient predictor of academic success, and that strong academic skills may serve as a protective factor for poor adaptability.

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 score0.158
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.035
GPT teacher head0.424
Teacher spread0.389 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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