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Record W4383878950 · doi:10.1080/09639284.2023.2232342

Academic and non-academic factors explaining anxiety among accounting students: evidence from the COVID-19 pandemic

2023· article· en· W4383878950 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAccounting Education · 2023
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAnxietyPsychologyPandemicContext (archaeology)WorkloadHigher educationSocial distanceDistancingMedical educationCoronavirus disease 2019 (COVID-19)Mathematics educationPolitical scienceMedicineManagementEconomics

Abstract

fetched live from OpenAlex

Studies have demonstrated the presence of anxiety among undergraduate students. Some causal factors are academic, but many are non-academic. The pandemic changed the way education is delivered, requiring remote learning for all. This situation disrupted students’ academic routines and presented significant learning challenges, causing anxiety. The pandemic also exacerbated the impact of non-academic factors, given the social distancing imposed. Based on a structural model analyzing 348 undergraduate accounting student responses, results show that a combination of academic and non-academic factors triggered anxiety among accounting students in the e-learning pandemic context. The items loading on the most important anxiety-inducing academic factor, namely teaching/learning challenges, suggest that the most basic teaching practices related to planning course workload and management should be considered in all circumstances and delivery modes. The paper offers academia ways to better prepare for the new learning modalities in accounting education or during a future pandemic.

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.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.085
GPT teacher head0.416
Teacher spread0.331 · 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