Perceived barriers to care and access to university mental health support services among first-year undergraduates: findings from the U-Flourish study
Bibliographic record
Abstract
OBJECTIVE: = 443) with unmet mental health needs. METHODS: Logistic regression tested which perceived practical, attitudinal, and stigma-related barriers predicted service use. Subgroup analyses targeted screen-positives for anxiety (GAD-7) and/or depression (PHQ-9). RESULTS: Reduced service use was linked to attitudinal barriers, minimizing problems (OR = 0.64; CI = 0.42-0.98) and difficulty discussing problems (OR = 0.59; CI = 0.38-0.91), especially among screen positives for the latter (OR = 0.49; CI = 0.27-0.89); practical barriers, uncertainty about how to get help (OR = 0.64; CI = 0.42-0.97) and time limitations (OR = 0.66; CI = 0.44-0.98), especially in screen-positives for both (OR = 0.46; CI = 0.26-0.79; OR = 0.47; CI = 0.27-0.79); and stigma-related barriers, feeling ashamed (OR = 0.63; CI = 0.40-0.98), appearing weak (OR = 0.65; CI = 0.42-0.98), and friends' reactions (OR = 0.58; CI = 0.38-0.88). CONCLUSIONS: Multiple perceived barriers were associated with a reduced likelihood of accessing university mental health services. Developing mental health literacy and streamlined pathways may improve timely support access for students with unmet needs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".