Evaluating a positive mental health programme for students
Bibliographic record
Abstract
With the number of students presenting at university mental health services increasing, Nicola Burford and Sheila Hardy set up a pilot project to equip them with the skills to self-manage their mental health Background: The past 20 years have seen the number of students attending university double in the UK. More than a quarter of students report having mental health problems, with the majority being anxiety and depression, and the number of suicides continues to increase. Students should be able to go to university and feel supported and well looked after, with access to timely, appropriate services and resources, and be empowered to develop skills to self-care. The aim of this project was to equip students attending one university in England with the skills to self-manage their mental health and seek further help when needed. Method: A programme of education called ‘HeadsUp!’ was developed which included six sessions: practical preventative information; common problems for students part 1; common problems for students part 2; self-help methods and local services and support; unhealthy behaviours; and Look After Your Mate, based on the Student Minds Campaign. The sessions were offered on a Thursday evening for up to 10 students and it was advertised through flyers, posters and Tweets. A place could be reserved through an online booking system. The sessions were evaluated using questionnaires. Results: The education programme was developed successfully. Advertising appeared to be effective as a total of 16 students signed up to one or more sessions. Between four and eight booked for five sessions, however, no one booked for the last session. Between one and three students attended the sessions. They scored the effectiveness of all five sessions as 4.3 out of a maximum score of 5. Next steps: Practice staff will get involved in open days and the Freshers Fair, and use social media as a way of making sure students are aware of the groups. The sessions will run again over the next academic year and will be amended according to the feedback received. They will be held on the university site and earlier in the academic year to improve attendance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".