Mental health portal : The student’s psychoemotional support
Bibliographic record
Abstract
Background: The prevalence of students’ mental health problems has increased in recent years. The digitalization of mental health care has made it possible to connect with students in new ways, hence, more focus has been placed on mental health portals. However, the usability of the portal is crucial to ensure that it meets the student’s needs and expectations. Aims: This study aims to develop a mental health portal using Rapid Application Development (RAD), and subsequently to evaluate its usability. Methods: Heuristic Evaluation and System Usability Scale (SUS) were conducted to assess the expert’s and 189 students’ experience in manoeuvring the portal, respectively. Design and content improvisation were made based on the expert’s insight. Results: The portal’s usability score was 71.11, which indicates good usability. A score above 70 indicates high usability, whereas a score below 70 indicates poor usability and requires further improvement. The results of this study offer the first evidence about the usability of the mental health portal but there is still potential for advancement. Conclusions: Developing an engagement-enhancing mental health portal is essential. The portal should strive to consolidate and present resources in a navigable and accessible manner to suit the needs and expectations of students seeking mental health support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".