Building a children's health and environment research agenda in Alberta, Canada: A multi-stakeholder engagement process
Bibliographic record
Abstract
As new environmental exposures are continuously identified, environmental influences on health are of growing concern. Knowledge regarding the impacts of environmental exposures is constantly evolving and is often incomplete. In this paper, we describe a multi-phased, multi-stakeholder engagement initiative involving diverse stakeholders with an interest in building a children's environmental health research agenda which would link with and support local practices and policies. The intent of this initiative was to identify priority research issues, themes and questions by implementing a tested Research Planning Model that encompassed the engagement of diverse stakeholders. Here, we describe the model application, which was specifically focused on children's health and the environment. A key component of the model was the ongoing stakeholder engagement process. This included two stakeholder forums, during which participants identified three main research themes (social determinants of health, environmental exposures and knowledge translation) and a short list of research questions. Other key components of the model included the development of a Global Sounding Board of key stakeholders, an Advisory Board and a Scientific Panel with mandates to review and prioritise the research questions. In our case, the Advisory Board and Scientific Panel prioritised questions that focused on environmental exposures related to children's respiratory outcomes. The stakeholder engagement described here is an evolving process with frequent changes of context, sustained by the commitment and dedication of the Children's Environment and Health Research planning team and the Advisory Board. In this article, we share the engagement process, outcomes, successes, challenges and lessons learned from this ongoing experience. Keywordsstakeholder engagement, children's health, environmental health, health research
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".