The Influence of Users' Information Needs Regarding Diabetes on Their Intention to Use Health Platforms
Bibliographic record
Abstract
In Indonesia, diabetes mellitus ranks as the third most common cause of death in 2019, has seen a rapid increase in cases over the years. With technological advancements, people now have easier access to various health services through the internet. Given the increasing trend of diabetes-related online searches, such as ‘diabetes’ and ‘sugar disease’, this research investigated the factors driving the intention to use digital health platforms among people in Greater Jakarta. To test the measurement model and the hypothesis model, a two-stage structural equation modeling method was used. The data collected were respondents who used the health application and data of 302 respondents through questionnaires (April-May 2024) distributed who live in Jakarta and surrounding areas in Indonesia were collected through purposive sampling and processed with SMARTPLS 4. Key findings indicate that health information seeking intention, social influence, and performance expectancy significantly influence intention to use digital health platforms. Perceived ease of use has a significant impact on perceived usefulness, while perceived trust is critical to health information seeking behavior. However, perceived usefulness and effort expectancy are insignificant in influencing intention to use these platforms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".