Evaluating a combined intervention targeting at‐risk post‐secondary students: When it comes to graduating, mental health matters
Bibliographic record
Abstract
Abstract Amid reports of the surging mental health crisis among students, a related area of concern for many post‐secondary institutions is retention rates. Mental distress has been shown to impact academic functioning, leading to decreased academic performance and dropout. This quantitative study evaluated a 12‐week combined counselling intervention programme designed to improve both the mental health and academics of 244 self‐referred, at‐risk students at a Canadian university. Differences pre‐ and post‐programme were examined among the following groups: those who were struggling academically, those who were mentally distressed and those who were experiencing both issues. Mental health, academic functioning and academic performance were measured pre‐ and post‐programme by means of Likert‐style questionnaires and overall grade point average (GPA). Results of paired‐samples t tests demonstrated that all groups experienced significant improvement in academic performance, academic functioning and mental health. Almost all students who presented to the programme on academic warning were able to increase their grades in order to remain in their programmes, avoiding suspension. Findings demonstrate the programme's potential to provide support for university students who are struggling both academically and mentally, as well as increasing student retention rates. Discussion of these results highlights the implications for the implementation of holistic, combined intervention approaches within university policy to increase student retention rates and answer student calls for increased mental health support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".