The Potential for Improving the Population Health Effectiveness of Screening: A Simulation Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Screening interventions in pediatric primary care often have limited effects on patients' health. Using simulation, we examined what conditions must hold for screening to improve population health outcomes, using screening for depression in adolescence as an example. METHODS: Through simulation, we varied parameters describing the working recognition and treatment of depression in primary care. The outcome measure was the effect of universal screening on adolescent population mental health, expressed as a percentage of the maximum possible effect. Through simulations, we randomly selected parameter values from the ranges of possible values identified from studies of care delivery in real-world pediatric settings. RESULTS: We examined the comparative effectiveness of universal screening over assessment as usual in 10 000 simulations. Screening achieved a median of 4.2% of the possible improvement in population mental health (average: 4.8%). Screening had more impact on population health with a higher sensitivity of the screen, lower false-positive rate, higher percentage screened, and higher probability of treatment, given the recognition of depression. However, even at the best levels of each of these parameters, screening usually achieved <10% of the possible effect. CONCLUSIONS: The many points at which the mental health care delivery process breaks down limit the population health effects of universal screening in primary care. Screening should be evaluated in the context of a realistic model of health care system functioning. We need to identify health care system structures and processes that strengthen the population effectiveness of screening or consider alternate solutions outside of primary care.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".