Enhancing surveillance strategies for childhood self-reporting data
Bibliographic record
Abstract
Problem Investigators routinely incorporate youth self-report data into cohort studies with variable results. Our study successfully employed strategies to ensure data quality and reduce participant fatigue. Methods A longitudinal study of rural youth aged 9–18 years was conducted to develop multivariable risk prediction models of agricultural related injuries at the same time collecting unintentional injury data for children exposed to agricultural hazards. Over 400 youth were involved in a 13-week cohort study. During this time period, each person interacted with researchers in a multitude of ways to remain engaged in the study, avoid reporting fatigue, and deliver quality self-reported data. Results To obtain study results, youth (and their consenting parents) received letters of invitation. Once enrolled, the study team met face-to-face with each subject to describe the reporting process. Complimentary to completing a daily record book, youth had weekly interaction with an electronic toll free phone system. If the youth did not phone in, the system called them until contact was made. Additional incentives included a personal wristwatch, monetary incentives for each week data was submitted, and lottery opportunity to win sport tickets. Conclusion Semilogarithmic plots of rates of all unintentional injuries (US data from 2000) as well as agriculture-related injuries (US and Canadian data from 19 previous studies) graphed as a function of injury severity exhibited linearity. Our cohort reported injury rates 1.4 to 4.3 times higher than national rates, suggesting that our methodology can significantly reduce injury under-reporting.
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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 | 0.307 | 0.507 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".