Defining Health Promotion Clearly for Teaching it Precisely: a proposal
Bibliographic record
Abstract
held in Mak’1lhari. Japan in 1995, the interest in taking a comparative look at advanced training programmes in the fields of health promotion and education was identified as a central concern of the participants of the Global Network Development (GND) sessions that convened academics. At the same meetings, several people wanted also to identify the elements of a &dquo;model programme&dquo; that could possibly inspire places wanting to start one. Since then, diverse activities related to this issue have been undertaken, in a more or less coordinated manner, in diverse places all over the globe. Following are several examples. For the Americas, the Interamerican Coalition of Universities and Centres for Training of Health Education and Health Promotion Professionals was created in 1996 as an initiative of the PanAmerican Health Organisation and the Latin American Regional Office of the IUHPE (ORLA)’. It has, among other things, started an inventory banking on approaches such as the one developed in Canada by the Canadian Consortium for Health Promotion Research, which carried out an exhaustive inventory of university training in this field offered throughout the country. ’ For the 4th International Conference on Health Promotion held in Jakarta, July, 1997, an electronic call was made throughout the planet, in order to attempt to identify the content of higher education programmes in health promotion. The results are available at the
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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 | 0.075 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.057 | 0.043 |
| Insufficient payload (model declined to judge) | 0.013 | 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".