Students??? perception of the online self-assessment support tools/information on depressive related disorders.
Bibliographic record
Abstract
Background: Mental health issues are prevalent among students; research has found that\nuniversity students experience significantly higher levels of psychological distress than\nthe general population. However, research on help seeking behavior has found that\ndistressed individuals are less likely to seek professional help. This study will examine\nAttitudes toward online mental health resources and identify the effects of external\nfactors such as Social Influence and Online Tool Designs on its usage.\nResults: We conducted a two-part survey about students??? perception of online mental\nhealth resources. We modified the Technology Acceptance Model (TAM); an\ninformation system theory that models how users come to accept and use a technology.\nConclusions: Results indicate TAM-Mod predicts a substantial proportion of Intention to\nUse online mental health resources. Although not all external factors are significant,\nresearch indicates that external variables are contributing factors in the individual\ndecision to access and utilize online mental health resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".