Applying the Social Determinants of Health to Understand the Impacts of Engineering on Indigenous Communities
Bibliographic record
Abstract
Public and population health have recently received increased attention in society. Specifically, the social determinants of health, which describe 12 social and economic factors have been used as a framework to understand the complexity of health. Engineering has a unique position in society to influence these factors, however, there has been limited appreciation for the opportunities for engineering projects to influence the broader determinants of health and to incorporate these concepts into engineering education, despite the recognition from health and public health professionals of their importance. The purpose of this narrative participatory action research study is to explore the connection between engineering and the social determinants of health in two First Nations communities in [redacted]. Narrative conversations with two researcher-participants from [redacted]and [redacted] were had over three months to listen to the lived experiences of these community members and explore the impact of engineering on health. Their stories demonstrate that engineering has the potential to greatly affect the physical environment, and in turn influence population health. As engineers, it can be difficult to consider the socioeconomic effects of engineering projects on stakeholders due to the at times, indirect cause and effect relationship. This study identifies opportunities for engineers to consider and improve some aspects of population health but leaves us to wrestle with the impact of engineering on complex social elements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".