The pitfalls of scaling up evidence-based interventions in health
Bibliographic record
Abstract
Policy-makers worldwide are increasingly interested in scaling up evidence-based interventions (EBIs) to larger populations, and implementation scientists are developing frameworks and methodologies for achieving this. But scaling-up does not always produce the desired results. Why not? We aimed to enhance awareness of the various pitfalls to be anticipated when planning scale-up. In lower- and middle-income countries (LMICs), the scale-up of health programs to prevent or respond to outbreaks of communicable diseases has been occurring for many decades. In high-income countries, there is new interest in the scaling up of interventions that address communicable and non-communicable diseases alike. We scanned the literature worldwide on problems encountered when implementing scale-up plans revealed a number of potential pitfalls that we discuss in this paper. We identified and discussed the following six major pitfalls of scaling-up EBIs: 1) the cost-effectiveness estimation pitfall, i.e. accurate cost-effectiveness estimates about real-world implementation are almost impossible, making predictions of economies of scale unreliable; 2) the health inequities pitfall, i.e. some people will necessarily be left out and therefore not benefit from the scaled-up EBIs; 3) the scaled-up harm pitfall, i.e. the harms as well as the benefits may be amplified by the scaling-up; 4) the ethical pitfall, i.e. informed consent may be a challenge on a grander scale; 5) the top-down pitfall, i.e. the needs, preferences and culture of end-users may be forgotten when scale-up is directed from above; and 6) the contextual pitfall, i.e. it may not be possible to adapt the EBIs to every context. If its pitfalls are addressed head on, scaling-up may be a powerful process for translating research data into practical improvements in healthcare in both LMICs and high-income countries, ensuring that more people benefit from EBIs.
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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 | 0.734 | 0.870 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.028 | 0.047 |
| Open science | 0.013 | 0.026 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 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".