Analyzing an Emerging Field of Public Health Practice in Ontario, Canada: The Case of Climate Change Adaptation
Bibliographic record
Abstract
This dissertation uses the case of public health adaptation to climate change in Ontario, Canada to develop an understanding of how new fields of public health practice emerge, and how practices become legitimated by practitioners. Since climate change became an identified public health issue by way of the Ontario Public Health Standards, 2008 (OPHS), and given that health equity is a normative dimension of practice and climate change holds the potential to exacerbate existing health inequalities, this work makes three primary contributions. First, I utilize Bourdieu’s theory of practice to describe how social change occurs within the professional field of public health. Second, I share practitioner interpretations of the OPHS to develop an understanding of how policy on climate change adaptation is translated into practice. Third, I highlight the role of health equity in climate change work, as practiced by Ontario public health practitioners. The dissertation draws on data from a web-scan of the thirty-six Ontario public health unit web pages and twenty in-depth interviews with public health practitioners from twenty health units. By identifying specific practices and interpretations of public health policy related to climate change, I show how specific practices are imbued with discursive meaning that shape how public health action on climate change is framed and understood as ‘legitimate’. Findings illustrate that climate change is very much an emerging field of practice, with a variety of approaches taken by practitioners including inaction, the repackaging of existing environmental health activities, and championing innovative practices. I demonstrate how practitioners selectively utilize policy to both enable and constrain public health action on climate change, and to document how health equity is employed as a discursive strategy by the champions of this work to try and legitimate climate change adaptation as a unique sub-field of public health practice.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.048 | 0.040 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".