Intersectoral action for health equity as it relates to climate change in <scp>C</scp>anada: contributions from critical systems heuristics
Bibliographic record
Abstract
RATIONALE: Intersectoral action (ISA) has been at the forefront of public health policy discussions since the 1970s. ISA incorporates a broader perspective of public health issues and coordinates efforts to address the social, political, economic and environmental contexts from which health determinants operate and are created. Despite being forwarded as a useful way to address and treat complex or 'wicked' problems, such policy issues are still often addressed within, rather than across, disciplinary silos and ISA has been documented to fail more often than it succeeds. AIMS AND OBJECTIVES: This paper contributes to an understanding of ISA by outlining and applying critical systems heuristics (CSH) theory and methods. METHODS: CSH theory and methods are described and discussed before applying them to the example of addressing climate change and health equity through public health practice. RESULTS: CSH thinking provides useful tools to engage stakeholders, question relations of power that may exist between collaborating partners, and move beyond power inequalities that guide ISA initiatives. CONCLUSIONS: CSH is a compelling framing that can improve an understanding of the collaborative relationships that are a prerequisite for engaging in ISA to address complex or 'wicked' policy problems such as climate change.
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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 | 0.052 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".