Awareness, Access and Use of Internet Self-Help Websites for Depression by University Students
Bibliographic record
Abstract
BACKGROUND: University students have a higher prevalence rate of depression than the average 18 to 24 year old. Internet self-help has been demonstrated to be effective in decreasing self-rated measures of depression in this population, so it is important to explore the awareness, access and use of such self-help resources in this population. OBJECTIVE: The objective of this study is to explore university students' awareness, access and use of Internet self-help websites for depression and related problems. METHODS: A total of 2691 university students were surveyed at 3 time points. RESULTS: When asked about browsing behavior, 69.6% (1494/2146) of students reported using the Internet for entertainment. Most students were not familiar with self-help websites for emotional health, although this awareness increased as they completed further assessments. Most students considered user-friendliness, content and interactivity as very important in the design of a self-help website. After being exposed to a self-help website, more students reported visiting websites for emotional health than those who had not been exposed. CONCLUSIONS: More students reported visiting self-help websites after becoming aware of such resources. Increased awareness of depression and related treatment resources may increase use of such resources. It is important to increase public awareness with the aim of increasing access to targeted strategies for young people.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".