Bibliographic record
Abstract
We study the determinants of poor mental health among students at an elite private institution.Survey measures of well-being have declined significantly over the last decade for both high school students and those of college age.This is an international phenomenon that appears to have started in the US around 2013 and that was not caused by but was exacerbated by COVID and the associated lockdowns.We focus on elite and non-elite institutions and examine Dartmouth as a special case.Dartmouth ranks well compared to other institutions.However, around a quarter of Dartmouth students (26%) report they suffer from moderate to severe depression and 22% that they suffer from moderate, to severe, anxiety and 10% say they contemplated suicide.Student's wellbeing appears to be impacted negatively by stress over finances.We find broad patterns in the data, that ill-being is higher among females, those who engage in little exercise, have low GPAs, are not athletes nor in academic clubs nor religious organizations, reside in fraternity housing or are on financial aid.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".