Bibliographic record
Abstract
BACKGROUND: The developed world responds to new and re-emerging diseases through the discovery of medications. Disease can be transmitted around the world in a day, but the development of medications does not occur at this rate. The world has one environment and the focus in health care must be on identifying factors in this environment that coalesce to produce disease. AIM: The aim of this paper is to introduce the integrative model of environmental health and explore its potential to illuminate the Toronto SARS experience. DISCUSSION: SARS affected people on three continents in a matter of days. Response to this new disease varied from one area to another and was dependent upon the level of integration of health services and communication across services. The present focus of the health care system is on treating the results of disease rather than the causative factors. Reacting to a new disease had grave social and economic consequences. The time for a new global environmental approach to health is now. The Toronto SARS experience was examined using the integrative model of environmental health and the upstream perspective as exemplars to interrupt the traditional approach to disease. All health care providers share the responsibility to learn about and to understand how our environment creates disease. This knowledge comes through research on topics such as; chemicals, pesticides, soil erosion, killing of forests, contamination of water, destabilization of climate, and social disruption from wars. CONCLUSIONS: Health care systems in the developed world continue to focus on the treatment of disease. A global ecological initiative for an integrated disease prevention system must be negotiated among nations.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".