MétaCan
Menu
Back to cohort
Record W4380481952 · doi:10.6000/1929-4409.2020.09.300

Academic Procrastination, Self-Esteem and Self-Efficacy in University Students: Comparative Study in Two Peruvian Cities

2022· article· en· W4380481952 on OpenAlexvenueno aff
Yrene Cecilia Uribe Hernández, Oscar Fernando Alegría Cueto, Nikita Shardin-Flores, Carlos Luy-Montejo

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProcrastinationMetropolitan areaSelf-esteemScale (ratio)PsychologyPopulationSample (material)Social psychologySelf-efficacyPositive relationshipDemographyMedical educationClinical psychologyMedicineSociologyGeography

Abstract

fetched live from OpenAlex

The present study aims to determine the relationship between academic procrastination, self-esteem and self-efficacy in undergraduate students in two Peruvian cities. The population consisted of 13,767 students, from which a sample of 1,494 was extracted. The subjects were selected from eight universities: five private and one public, from the city of Metropolitan Lima; and two universities, one public and one private, from the city of Arequipa. The instruments used were the Academic Procrastination Scale (EPA), the Scale of Specific Perceived Self-Efficacy in Academic Situations (EAPESA) and the Rosemberg Self-Esteem Scale. The results allow us to conclude that, in terms of perceived effectiveness, the relationship is slightly higher in the city of Arequipa, reiterating this with respect to academic procrastination, where the relationship is also slightly higher. Finally, with regard to self-esteem, the trend continues to indicate a greater relationship in Arequipa

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)0.001
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.393
Teacher spread0.330 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Criminology and SociologySame topicPerfectionism, Procrastination, Anxiety StudiesFrench-language works237,207