Perceived Support and Gender: Identifying with the Engineering Community as a First-Year Engineering Student
Bibliographic record
Abstract
National interest in mental wellbeing in the Canadian population has trickled down to focusing on subsets of the population that are particularly vulnerable to poor mental wellbeing. One of these subsets is the engineering student population due to the high stress and anxiety associated with their course load and prospects. The current study carried out a secondary analysis of wellbeing surveys administered to engineering students (N = 141) during the Winter 2020 semester. The primary analysis sought to determine whether perceived peer support, instructor support, and staff support predicted engineering identity. Greater identification with one’s career path is shown to be related with greater wellbeing in students and employees in the form of greater satisfaction and likelihood to remain in the degree program. Further, the analysis explored whether gender and hometown acted as moderating variables, either intensifying or lessening the main relationship. The analysis uncovered a statistically significant relationship between perceived peer support and engineering group identity, r = .534, p <.001. This relationship was moderated by gender, p = .033, wherein female engineering students who reported low levels of peer support were far less likely to feel a sense of belonging in the engineering community than male students. This gender difference did not exist for those who reported high levels of perceived peer support. Implications for female representation and program development are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".