Disease Prevention, Including Early Detection of Illnesses (EPHO5)
Bibliographic record
Abstract
Abstract Digital public health (DiPH) interventions have been used to achieve public health disease prevention goals. As a practical example, we describe GetCheckedOnline, a publicly funded web-based testing service for sexually transmitted and blood-borne infections (STBBI) in British Columbia, Canada. This service has gained increasing relevance, especially among people experiencing marginalization who bear a disproportionate burden of STBBIs. It continues to contribute to the secondary and tertiary disease prevention of STBBIs by facilitating early diagnoses and treatment of infections, thereby disrupting their community transmission. In this chapter, we highlight the importance of applying the fundamental public health principle of health equity across the life cycle of DiPH interventions and suggest that a focus on health equity inevitably ensures the achievement of overarching disease prevention goals. We reflect on practical considerations for health equity across various phases of the planning and development, implementation, scale-up, adaptation, sustainment, and maintenance of these DiPH interventions. Further, we demonstrate the central and complementary roles that human-centered design and embedded research and evaluation play in prioritizing health equity across the phases of DiPH interventions. We also highlight key challenges encountered when implementing DiPH interventions that focus on health equity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".