Planning for healthy and equitable communities in British Columbia : a critical analysis of the implementation of an equity lens in Healthy Built Environments initiatives
Bibliographic record
Abstract
British Columbia’s communities, as settings in which we work, learn, and play, have a significant role in shaping our health and well-being. Recently, the provincial government has encouraged health authorities to join with local government planners to create local Healthy Built Environment (HBE) teams, so that they can work together for healthier communities. Within our communities, there are significant differences in health status that are unjust or unfair, and are rooted in underlying socio-political processes. International research suggests that we must reduce those inequities if we are to improve health for all. To help address health inequities, researchers have suggested that public health practitioners use an ‘equity lens’ in their day-to-day work. Yet implementing such a lens is challenging. This case study explored the implementation of an equity lens in HBE work in BC. The project examined the work of intersectoral HBE teams at the provincial and local levels, through an in-depth examination of HBE projects within three different BC communities. Data was collected through interviews, participant observation, and the collection of key documents, maps, and photographs. The main research question was: How is an ‘equity lens’ being implemented in association with Healthy Built Environments work in British Columbia? Influenced by the shifting strategic direction of the provincial government, HBE teams reported only limited progress in actively considering equity as integral to their work. The key elements of the implementation of an equity lens included targeting specific, ‘vulnerable’ populations and using community health data to monitor key outcomes. In general, however, the political will to more fully consider equity as integral to HBE work was just not present at either local or provincial levels. There was hope, however, in the form of champions, who worked to re-frame equity issues in more palatable ways, and the desire to explore new tools to better understand equity issues at the local level. There is also evidence of a growing desire within HBE teams to build meaningful, authentic, partnerships, consistent with a broad Healthy Communities approach. The development of those partnerships will be key to collectively building more just, inclusive, and healthier communities.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.053 | 0.023 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| 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".