High performing male and female engineering students in Chile: accounting for mental health and well-being from a developmental paradigm
Bibliographic record
Abstract
Mental health and well-being among high-performing male and female engineering students were investigated to account for variance. A self-report survey was used to assess mental health and well-being, and the results of the end-of-year evaluation were used to measure academic accomplishment. This study was unique in that it created the self-reported survey from a developmental viewpoint (i.e., developmental strengths, constructive skills, and psychological competencies) using the normative-crisis model and the psycho-social model of development. Of the 152 (121 male, 31 female) University students from Chile, twenty high-achieving male and female students were randomly selected. The findings showed that female students scored lower in all subjects, reported lower levels of hope and reported more mental health concerns than male students. Structural equation modelling (SEM) analysis of female students’ results found that lower hope levels and higher developmental strengths were associated with high academic achievement. However, mental health issues and psychological competencies among females did not influence higher achievement. In contrast, SEM analysis of male students’ results found no correlation between academic achievement and mental well-being, which suggests that high academic achievement is independent of sex differences, mental health and well-being. Insights, implications and recommendations 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".