Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to (i) describe the procedures for generating self-generated identification codes (SGICs) in a prospective longitudinal evaluation of a sexual health program for secondary school students in Hong Kong; (ii) outline the matching strategies and processes; (iii) examine rates of successful matching and associated factors; and (iv) compare the responses of participants whose data could be matched to those whose data could not. METHODS: A prospective longitudinal cohort study was conducted. The SGIC comprised a 5-element code with 4 digits and 3 letters. A matching algorithm was developed to link baseline and follow-up data collected from students in Years 1 to 3 (n = 1,064) during the 2019-2020 school year. Matching success and associated factors were analyzed, and responses from matched and unmatched participants were compared. RESULTS: The rate of perfectly matched cases was 49.06%, while 23.59% were partially matched, and 27.35% were unmatched. Logistic regression analysis revealed that male students (adjusted odds ratio [aOR]: 0.63) and Year 1 students (vs. Year 3; aOR: 0.56) were less likely to be perfectly matched. Compared to unmatched cases, perfectly and partially matched cases were less likely to have missing values and more likely to exhibit positive attitudes toward the sexual health program and related topics, such as the importance of sexual health, equal relationships, and condom use. CONCLUSION: The use of SGICs successfully matched approximately 72.65% of the study sample over a one-year period. These findings highlight the potential of SGICs as a tool for longitudinal data matching while underscoring the need for further refinement of code generation processes and matching algorithms to minimize data wastage and improve effectiveness.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".