Out of context? Translating evidence from the North Karelia project over place and time
Bibliographic record
Abstract
Within the literature on community-based heart health promotion and chronic disease prevention, the North Karelia project is often viewed as a model program for achieving community-wide reductions in risk factors and mortality associated with cardiovascular disease. In the present study, we examine the tendency to attempt replication of elements of the North Karelia project, without due consideration of the unique population and setting being targeted. We analysed a sample of 64 articles reporting on community-based interventions targeting chronic disease, published between 1990 and 2002. Of these 64 articles, 43 (67%) made explicit reference to North Karelia or one of the other early projects (Stanford, Minnesota, Pawtucket). Of these 43 articles, 8 (19%) explicitly acknowledged the unique features of the population/setting in question, and articulated a need to adapt to these unique features, while 10 (23%) provided no acknowledgment of unique population/setting features. The remaining 25 (58%) were 'in between', and examples from each group are discussed. We conclude that for many contemporary community-based interventions, concern with replicating the North Karelia project is accompanied by inadequate consideration or reporting of the details of the unique context (including people, place and time), and this may undermine the success of community-based health promotion.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".