The Development of “Friend from Heart” Application based on LINE System to Promote Well-Being of Undergraduate Students of Faculty of Education, Kasetsart University
Bibliographic record
Abstract
Depression and suicide rates among youths tended to increase. From reviews, many applications and online counseling could reduce depression and anxiety to promote well-being of youths and university students effectively. This study was conducted to develop "Friend from heart" application based on LINE system to promote well-being for undergraduate students of faculty of education, Kasetsart University. The research method included the survey of basic data for developing the application and evaluation of the application by specialists. A total of 72 voluntary students were invited to join an online survey. It was found that most of the students (81.94%) wanted applications that provide physical health information such as exercise, eating healthy food, and health care. However, about 16.66% of students needed an application that can speak or listen problems with video calls. Then, researchers took the services that students were interested more than 50% to develop the applications. It worked through the application, consisting of chatbot, physical health, mental health, and appointment with counselor. The index of item-objective congruence was 0.66-1.00 with additional specialists commenting that the application had an interesting design with good structure to help students. For ethical approval, it was obtained from the Kasetsart University Research Ethics Committee.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".