Bibliographic record
Abstract
In an increasingly globalized society, there is currently a record number of students from abroad studying in Canada (i.e., international students) (Government of Canada, 2012). Like any other student, international students are susceptible to mental health issues, such as anxiety, mood disorders, and substance abuse. Without intervention, these issues can lead to adverse academic, career, and social development (Kitzrow, 2003). A survey by the University of Idaho Student Counseling Center (2000) found that counselling treatment is useful for at least 77% of the clients who attend, by helping them gain the skills needed to overcome or work with their illness (as cited in Kitzrow, 2003). Even though most clients who use campus-based counselling services find it beneficial, studies have found that international students not only underutilize counselling services, but are more likely to drop out after the initial session (Chen & Lewis, 2011). Since the number of international students is increasing and counselling services have been shown to be useful for students from North America, one must wonder why international students do not use these services. What do international students’ impressions of campus-based counselling tell us about the ways we need to improve these services?
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".