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Record W4412870801 · doi:10.24908/pceea.2025.19723

Preliminary Survey Findings from a Study on the Attrition of Engineering Graduates from Engineering Practice

2025· article· en· W4412870801 on OpenAlexaffvenueabout
Paul Neufeld, Sean Maw

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAttritionMedical educationEngineeringSurvey researchEngineering ethicsPsychologyEngineering managementMedicineDentistryApplied psychology

Abstract

fetched live from OpenAlex

Career choices are among the most important decisions people make during their lifetime. Despite the more widely studied area of career intentions of engineering students, few studies have focused on engineering graduates' career decisions, especially in Canada. Furthermore, these few studies focus on “early careers” (i.e. within five years of graduation). To address this gap, a sequential mixed methods study was designed with the primary research question: Why do engineering graduates decide to leave engineering practice when they do? The initial quantitative phase involved developing and deploying a survey in Fall 2024 to 7,718 engineering alumni from the University of Saskatchewan. Initial results indicate similarities and differences between engineering graduates who are currently practicing engineering and those who are not. Regardless of the career path engineering graduates take, the data suggests that most individuals find their calling, including satisfying and fulfilling careers, whether that is in engineering practice or not.

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)0.007
metaresearch head score (Gemma)0.020
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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
Published2025
Admission routes3
Has abstractyes

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