Enhancing the effectiveness of the International Union for Health Promotion and Education to move health promotion forward
Bibliographic record
Abstract
The success in recent years of many IUHPE initiatives provides cause for celebration, but also reminds us of the challenges that lie ahead. The Global Programme for Health Promotion Effectiveness provides a blueprint for how the IUHPE can effectively participate in, and lead, global networks for health. Health promotion research is well organized and productive in most of the Northern hemisphere, but important wells of health promotion knowledge in the Southern hemisphere are not widely-enough disseminated. The IUHPE needs to help liberate knowledge producers everywhere from unnecessary structures, and find innovative ways to illuminate knowledge for all to see. We have developed and proven the effectiveness of a range of technologies such as settings-based health promotion. However, the vast majority of communities are untouched, and the IUHPE needs to be a leader in finding ways to better disseminate effective health promotion practice. The IUHPE is a vigorous and effective advocate for health promotion training, practice and research. Now we need to expand our advocacy for equity in health, building on our effective work on social clauses in trade agreements and on tobacco control.
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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 | 0.062 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".