Research Review: Help‐seeking intentions, behaviors, and barriers in college students – a systematic review and meta‐analysis
Bibliographic record
Abstract
Background The prevalence of mental health problems among college students has increased over the past decade. Even when mental health services are available, many students still struggle to access these services. This systematic review and meta‐analysis aimed to identify the rates at which students actively seek or consider using formal help and to determine the main reasons for not seeking help. Methods A comprehensive literature search was conducted on PubMed, PsycINFO, and Embase to identify studies on help‐seeking behaviors, intentions, and barriers to help‐seeking among college students with mental health problems. Random effect models were used to calculate the pooled proportions. Results Of the 8,919 identified studies, 62 met the inclusion criteria and were included ( n = 53 on help‐seeking behaviors, n = 21 on help‐seeking intentions, and n = 14 on treatment barriers). The pooled prevalence of active help‐seeking behaviors was 28% (179,915/435,768 individuals; 95% CI: 23%–33%, I 2 = 99.6%), and the aggregated prevalence of help‐seeking intentions was 41% (62,456/80161 individuals; 95% CI: 26%–58%, I 2 = 99.8%). Common barriers reported by students included a preference to address issues on their own, time constraints, insufficient knowledge of accessible resources, and a perceived lack of need for professional help. Conclusions The findings highlight the gap between the mental health needs of the students and their actual help‐seeking rates. Although personal barriers are common, systemic or contextual challenges also affect college students' help‐seeking behaviors.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".