2019 Special Issue: Psychological Wellbeing and Distress in Higher Education
Bibliographic record
Abstract
Many universities around the world have now initiated wellbeing strategies that encompass psychological wellbeing. These resources can be leveraged for change to better support students. Associate Professor Lydia Woodyatt from Flinders University, Adelaide and Dr Abi Brooker from the University of Melbourne are guest editors for this very special issue which includes a collection of articles from scholars and practitioners in Australia, Canada, the US, UK and Germany addressing student (and staff) psychological wellbeing in higher education. Broadly, articles discuss the scope of mental wellbeing and psychological distress, identify specific cohorts (including international students and refugees), profile targeted means of support (via the curriculum, the co-curriculum and strategic policy and planning initiatives) and also identify the need for ‘psychological literacy’ via leadership.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".