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Record W4388021794 · doi:10.1080/01443410.2023.2273759

Grades of university and college students decrease during the transition from high school to tertiary education: a latent change analysis of the 2 × 2 model of perfectionism

2023· article· en· W4388021794 on OpenAlexafffund
Patrick Gaudreau, Kristina Kljajić, Tim Fricker, Nicole Redmond

Bibliographic record

VenueEducational Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsMohawk CollegeUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerfectionism (psychology)PsychologyAcademic achievementTransition (genetics)Clinical psychologyDevelopmental psychologyChemistry

Abstract

fetched live from OpenAlex

Academic performance tends to deteriorate during the transition from high school to post-secondary education. In this study, our goal was to investigate whether the degree of this performance deterioration differs across the four subtypes of perfectionism from the 2 × 2 model of perfectionism. Samples of 392 university students and 946 college students completed a perfectionism questionnaire. Their high school and first semester grades were obtained through the office of the registrar. Results of latent change analyses revealed that the grades of students decreased during the transition to university (–12%) and college (–4%). The four subtypes of perfectionism were associated with a different degree of performance deterioration and supported the four hypotheses of the 2 × 2 model of perfectionism. Both university and college students with pure socially prescribed perfectionism experienced the strongest decrease in academic performance and should be closely monitored during their transition into post-secondary education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.329
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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