Methods of defining best practice for population health approaches with obesity prevention as an example
Bibliographic record
Abstract
Childhood obesity has reached a crisis stage and has become a population health issue. The few traditional systematic reviews that have been done to identify best practice provide little direction for action. The concept of evidence-based practice has been adopted in health care, and in medicine in particular, to determine best practice. Evidence-based medicine has its origins in the scientific method and for many researchers this concept means strict adherence to standards determining internal validity in order to justify a practice as evidence based. Practitioners addressing population health face challenges in identifying criteria for determining evidence, in part because of the nature of population health with its goal of shifting the health of whole populations. As well, the type of evidence provided by more traditional critical appraisal schema is limiting. Expanded approaches in finding and defining evidence have been proposed that use: expert panels; broad and inclusive search and selection strategies; appraisal criteria that incorporate context and generalizability. A recent synthesis of 147 programmes addressing childhood overweight and obesity provides a concrete example of using a broader approach to identify evidence for best practice (Flynn et al.2006). Incorporating evaluation and population health frameworks as criterion components in addition to traditional methodological rigour criteria, this synthesis has identified programmes that provide contextual information that can be used to populate what Swinburn et al. (2005) have described as the ‘promise table’. Using this approach a range in ‘certainty of effectiveness’ and a range in ‘potential for population impact’ are integrated to identify promising strategies. The exercise can provide direction for agencies and practitioners in taking action to address obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".