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Record W4386282446 · doi:10.1080/09639284.2023.2252809

A pandemic era study of accounting doctoral students

2023· article· en· W4386282446 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAccounting Education · 2023
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of GuelphLakehead University
FundersCanadian Academic Accounting Association
KeywordsAccounting researchPsychological interventionBurnoutPsychologyAccountingModerationAccrualMedical educationEarningsSocial psychologyBusinessMedicineClinical psychology

Abstract

fetched live from OpenAlex

In this study, we explore how accounting doctoral students fared during the pandemic. We survey accounting doctoral students from Canada and the United States and perform quantitative and qualitative analyses of the responses. We situate our research within social cognitive theory, and our findings suggest that accounting doctoral students experienced some stress and burnout due to exhaustion. Most students coped using healthy strategies; however, we highlight correlations between stress and burnout for those who didn’t. We also propose a series of possible interventions that can be adopted to support retention and future recruitment efforts. Some of these interventions reflect long-standing challenges faced by accounting doctoral students, such as increasing financial aid and having better-supported faculty to supervise students; however, many new challenges emerged because of shifts in doctoral studies during the pandemic and will require more innovative solutions.

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)0.001
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 score0.019
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.404
Teacher spread0.360 · 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