Measuring, monitoring and managing the psychological well-being of first year university students
Bibliographic record
Abstract
This paper profiles the psychological well-being of students in their initial year of university. There were three aims: to measure the impact of arrival at university on the psychological well-being of first year students, to monitor (i.e. profile) the shape of psychological well-being across the first year, and to investigate how students manage their well-being in relation to the use of university counselling services. Data were collected on four occasions, with 84% of all first year students at a UK university (4,699 students) completing the questionnaire on at least one occasion. Psychological well-being was assessed using the GP-CORE, a general population form of the CORE-OM. Results show that greater strain is placed on well-being once students start university compared to levels preceding entry. This strain rises and falls across the year but does not return to pre-university levels. Items tapping depression and anxiety suggest that the first year of university is a time of heightened anxiety but not a particularly depressive time. The findings are discussed in relation to students’ experience of higher education and how to match student needs with university counselling service provision.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".