Innoversity in knowledge-for-action and adaptation to climate change: the first steps of an 'evidence-based climatic health' transfrontier training program
Bibliographic record
Abstract
It has become increasingly clear to the international scientific community that climate change is real and has important consequences for human health. To meet these new challenges, the World Health Organization recommends reinforcing the adaptive capacity of health systems. One of the possible avenues in this respect is to promote awareness and knowledge translation in climatic health, at both the local and global scales. Within such perspective, two major themes have emerged in the field of public health research: 1) the development of advanced training adapted to 'global environment' change and to the specific needs of various groups of actors (doctors, nurses, public health practitioners, health care managers, public service managers, local communities, etc) and 2) the development of strategies for implementing research results and applying various types of evidence to the management of public health issues affected by climate change. Progress on these two fronts will depend on maximum innovation in transdisciplinary and transsectoral collaborations. The general purpose of this article is to present the program of a new research and learning chair designed for this double set of developmental objectives - a chair that emphasizes 'innoversity' (the dynamic relationship between innovation and diversity) and 'transfrontier ecolearning for adaptive actions'. The Écoapprentissages, santé mentale et climat collaborative research chair (University of Montreal and Quebec National Public Health Institute) based in Montreal is a center for 'transdisciplinary research' on the transfrontier knowledge-for-action that can aid adaptation of the public health sector, the public mental health sector, and the public service sector to climate change, as well as a center for complex collaborations on evidence-based climatic health 'training'. This program-focused article comprises two main sections. The first section presents the 'general' and 'specific contexts' in which the chair emerged. The 'general context' pertains to the health-related challenge of finding ways to integrate, transfer, and implement knowledge, a particularly pointed challenge in Canada. The 'specific context' refers to the emerging research field of adaptation of public health to climate change. In the second section, the characteristics of the research chair are more extensively detailed (the vision of 'innoversity' and ' transfrontier knowledge-for-action,' the approach of shared responsibility and complex collaboration, objectives, and major axes of research). We conclude with a call for complex collaboration toward knowledge-for-action in public health services/mental health services/public services' adaptation to climate change: this call is aimed at individual and institutional actors in the North and South/West and East concerned by these issues.
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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 | 0.184 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".