A theory-based approach to designing interventions for Planetary Health
Bibliographic record
Abstract
The current existential crises crystallize an urgent need for us all to contribute to meeting international environmental and social commitments. The message is clear: we need to take action. However, one of the challenges for decision-makers leading the transition is the dearth of practical tools and approaches available. Even in our field, evaluations are still based on practices which systematically overlook important determinants of human health, neglecting what matters most for our societies to thrive. This article aims to build on existing knowledge of program theories, theories of change, and theory-based evaluations to create a practical approach to designing interventions, while taking into account human and natural systems: what is referred to as evaluating for Planetary Health. A key purpose is to explore how we can conceptualize and elaborate interventions, taking into account their implications for Planetary Health, to suggest improvements or alternatives to existing programs, projects, or policies.
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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 | 0.065 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".