Design, Develop, and Pilot-Test a Digital Platform to Enhance Student Well-Being: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Well-being is a multidimensional concept and has been extended to many areas. Student well-being has garnered attention over the last decade due to concerns that have been raised. Digital health interventions have the potential to enhance and improve student well-being. OBJECTIVE: The objective of the study is to design, develop, and pilot-test a digital health platform to enhance student well-being. METHODS: A sample size of 5000 participants will be recruited across Gujarat and Tamil Nadu, India. Students will be enrolled from Parul University in Vadodara, Gujarat, as well as Panimalar Medical College Hospital and Research Institute, Panimalar Engineering College, Panimalar Institute of Technology, and Panimalar College of Nursing in Chennai, Tamil Nadu. Current undergraduate and graduate students consenting to participate will be recruited using convenience sampling from these institutes. The study will collect baseline data to construct the student well-being index. Based on the risk profile, a random subset of the population will be provided access to the digital health intervention, which will deliver tailored interactive messages addressing the various dimensions of well-being among undergraduate and graduate students. The eligible study participants will be aged 18 years and older, enrolled in these institutes, and willing to give their consent to participate in the study. RESULTS: The proposed research is an unfunded study. The enrollment of the individuals in the study began in October 2022. Data gathered will be analyzed using SAS (version 9.3; SAS Institute) and results will be reported as 95% CIs and P values. CONCLUSIONS: The proposed study will help to determine the factors affecting well-being among college students and help in designing digital health interventions to improve the well-being of undergraduate and graduate students. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39779.
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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 | 0.053 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".