Innovators and Early Adopters of Population Health in Healthcare: Real and Present Opportunities for Healthcare–Public Health Collaboration
Bibliographic record
Abstract
The population health approach - which has long been a cornerstone ideology for those in public health - is increasingly embraced by various actors in the healthcare sector, including but not limited to those in primary healthcare. There is much to be gained from public health's involvement in these early efforts, which could enable the pursuit of approaches that integrate both individual- and population-level interventions. These collaborative efforts could serve as the initial building blocks for transformative change, provided that others follow suit. Lessons from the study of the diffusion of innovation teach us that new ideas can spread or wither in unpredictable ways. However, the odds of success would be improved if we could (1) make population health concepts less complex and more actionable, (2) socialize early adopter activities and make them more observable to others so that they could help model the way, (3) invest in evaluation so that the benefits of the approach could be more clearly demonstrated and (4) address social and cultural barriers to adoption through opinion leaders within respective professional communities.
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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 | 0.032 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.078 | 0.072 |
| Insufficient payload (model declined to judge) | 0.006 | 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".