The Notijoves Project: Protocol for a Randomized Controlled Trial About New Communication Technologies and Gamification to Promote Partner Notification of Sexually Transmitted Infections Among Young People
Bibliographic record
Abstract
BACKGROUND: An increase in sexually transmitted infections (STIs) as well as an increase in the use of new information and communication technologies among young people in Catalonia is the inspiration behind the idea of designing a smartphone app to promote partner notification of STIs. OBJECTIVE: The main objective of this study is to design a Web-based tool adapted to smartphones for partner notification of STIs among youth who are 16 to 24 years old. Additionally, the objective is to evaluate the Web-based tool's role in increasing the patient referral partner notification. METHODS: This is a multicenter randomized controlled trial with a proportional stratification of the sample by center and random allocation of participants to the 3 arms of the study (simple Web-based intervention, game Web-based intervention, and control). This study is being conducted by midwives, gynecologists, and physicians in the sexual and reproductive areas of the primary health care centers. RESULTS: The primary outcome measure is the number and proportion of partner notifications. Additional outcome measures are the yield of early diagnosis and treatment of those exposed and infected, acceptability, barriers, and preferences for partner notification. Expected results include an increase in the yield of partner notification, early diagnosis and treatment among youth using Web-based interventions compared with those receiving the traditional advice to notify, and a description of sexual networks among those participating in the study. CONCLUSIONS: The Notijoves is expected to have a sustainable positive impact in the partner notification practice among youth and contribute to increasing the awareness of STI prevention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12896.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 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".