Intention to Use Leucorrheea Self-Test Innovation Kit and Collection Data through Mobile Application in Bangkok
Bibliographic record
Abstract
Leukorrhea, or abnormal vaginal discharge (fungi, bacteria, and protozoa), can be indicated bacterial vaginosis or STIs. If not treated, these infections may lead to complications impacted fertility such as pelvic inflammatory disease (PID). Therefore, the goal is to develop a self-test kit for vaginal discharge detecting all three pathogens in one device. This study examines the acceptance of innovative technologies and factors affecting the intention to self-administered leucorrhoea test kits (SALTK) with results presentation and data transmission via mobile phones. Factors influencing such intention included technology acceptance (Technology Acceptance Model: TAM), facilitating conditions, social influence, and perceived privacy risk. The data were collected using an online Google Form questionnaire. A total of 450 valid questionnaires were used for analysis. The descriptive statistics employed in this study included percentage, means, and standard deviation, and the hypothesis was tested using multiple regression analysis. It was found that the factors impacting the intention to use SALTK with a statistical significance level of .05 were facilitating condition, social influence, perceived privacy risk, and three key technology acceptance components, namely perceived usefulness, perceived ease of use, and attitude toward using, respectively. SALTK modulates the beneficial for public health policies in Thailand and government agencies, the Food and Drug Administration and the Department of Disease Control, establishing prevention to reach the international standards.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".