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Record W3036063765 · doi:10.24908/pceea.vi0.14154

THE “ENGINEERS HAVE FEELINGS” PROJECT: INTEGRATING MENTAL WELLNESS AND LIFELONG LEARNING SKILLS IN FIRST-YEAR UNDERGRADUATE ENGINEERING COURSES

2020· article· en· W3036063765 on OpenAlexaffvenue
R. Paul, Destiny Dedemus, Melissa Boyce, Kim L. Johnston

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCurriculumFeelingLifelong learningPsychologyIntervention (counseling)Medical educationPerceptionCoping (psychology)Student engagementPsychological resiliencePedagogyMedicine

Abstract

fetched live from OpenAlex

To address an identified need for programming that helps Engineering students to develop skills in coping, resilience, and lifelong learning, we designed, implemented, and reflected on the effectiveness of a curricular intervention on Engineering students’ mental wellbeing, academic engagement and achievement, and perceptions of support. This intervention has implications for the development of a model of the curricular components necessary to benefit undergraduate students’ academic engagement, success, and perceptions of student support. It may also be used to inform curriculum development across many university programs.

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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.234
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.264
Teacher spread0.258 · 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 teacher head, 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

Citations18
Published2020
Admission routes2
Has abstractyes

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