Implementing Health Impact Assessment as a Required Component of Government Policymaking: A Multi-Level Exploration of the Determinants of Healthy Public Policy
Bibliographic record
Abstract
It is widely understood that the public policies of ‘non-health’ government sectors have greater impacts on population health than those of the traditional healthcare realm. Health Impact Assessment (HIA) is a decision support tool that identifies and promotes the health benefits of policies while also mitigating their unintended negative consequences. Despite numerous calls to do so, the Ontario government has yet to implement HIA as a required component of policy development. This dissertation therefore sought to identify the contexts and factors that may both enable and impede HIA use at the sub-national (i.e., provincial, territorial, or state) government level.\nThe three integrated articles of this dissertation provide insights into specific aspects of the policy process as they relate to HIA. Chapter one details a case study of purposive information-seeking among public servants within Ontario’s Ministry of Education (MOE). Situated within Ontario’s Ministry of Health (MOH), chapter two presents a case study of policy collaboration between health and ‘non-health’ ministries. Finally, chapter three details a framework analysis of the political factors supporting health impact tool use in two sub-national jurisdictions – namely, Québec and South Australia.\nMOE respondents (N=9) identified four components of policymaking ‘due diligence’, including evidence retrieval, consultation and collaboration, referencing, and risk analysis. As prospective HIA users, they also confirmed that information is not routinely sought to mitigate the potential negative health impacts of education-based policies. MOH respondents (N=8) identified the bureaucratic hierarchy as the brokering mechanism for inter-ministerial policy development. As prospective HIA stewards, they also confirmed that the ministry does not proactively flag the potential negative health impacts of non-health sector policies. Finally, ‘lessons learned’ from case articles specific to Québec (n=12) and South Australia (n=17) identified the political factors supporting tool use at different stages of the policy cycle, including agenda setting (‘policy elites’ and ‘political culture’), implementation (‘jurisdiction’), and sustained implementation (‘institutional power’).\nThis work provides important insights into ‘real life’ policymaking. By highlighting existing facilitators of and barriers to HIA use, the findings offer a useful starting point from which proponents may tailor context-specific strategies to sustainably implement HIA at the sub-national government level.
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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 | 0.067 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".