University Counselling and Psychological Services: \nExploring Current Approaches and Student Perceptions
Bibliographic record
Abstract
Across Canada, university counselling and psychological services have experienced an increase in both student demand for assistance and complexity of presenting issues. The literature suggests that many universities are struggling to meet the needs of their students and are exploring various systems and methods (e.g., triage systems, online services) to address the need. Further, students’ perceptions of the adequacy and availability of these services are not well represented in the literature. The present study: 1) explored the on-campus counselling and psychological services offered at 55 Canadian universities through analysis of their online information; 2) investigated students’ perceptions of counselling and psychological services at Grenfell Campus, MUN, through a sample of 204 participants (159 women, 20 men, and 25 unspecified) with a \nmean age of 20.79 years (range: 18-42). The results indicated that while the majority of participants were aware of Grenfell’s counselling and psychological services (78.9%), a much smaller percentage (19.6%) used the services. Student perceptions were analyzed across a wide range of domains, including type of service, expectations of service options, barriers to accessing the service, and the adequacy and efficacy of additional mental health resources, in relation to the Canadian university context.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".