Bibliographic record
Abstract
Introduction: Universities are the ideal place to promote health because it offers facilities to provide comprehensive care as marked by the World Health Organization (1986) in the Ottawa Charter for health promotion. This is how this work seeks to describe the health promotion programs of universities in Mexico to generate relevant information on this particular topic. Objective: To describe the health promotion programs of higher education institutions in Mexico. Methodology: The review was carried out in PUBMED, CROSSREF, LILACS, SCIELO and government institutions indexing sources. For the selection of articles, it was considered to analyze government pages such as RMUPS and public and private universities due to lack of information. The review criteria that were taken into account were the objective, areas of attention, type of user, level of prevention and finally operation of the health promotion programs of the universities. Results: Of the programs of institutions of higher education reviewed it was found that they have similarities regarding their objectives where the majority promote the adoption of healthy lifestyles, in addition to the fact that a wide group of services such as medical care, nutritional care, and psychological care as the main, the operation of the program is diverse and 100% manage a level of primary prevention. Conclusions: There is a clear and direct interests by the authorities of the different universities in Mexico, there are currently 43 institutions that make up the RMUPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".