Case 9 : Achieving Health Equity in Ontario: Increasing Capacity for Relationship Building with Indigenous Communities
Bibliographic record
Abstract
Paul Green is concerned that his organization is not meeting the requirements of the modernized Ontario Public Health Standards’ Health Equity Standard after his colleagues ask for direction on working with local Indigenous communities. Under the third requirement of the new Health Equity Standard, all boards of health must engage with Indigenous communities and organizations, which must include the “fostering and creation of meaningful relationships”. As the new Health Equity Manager at Turtle Creek Public Health (TCPH), Paul is tasked with developing a set of recommendations for organizational action. After receiving advice from a colleague, Paul decides that the next step for his organization is to conduct a situational assessment to explore how it may effectively, appropriately, and meaningfully build relationships with local Indigenous communities. However, Paul is unsure about where to begin. What questions should be asked? What important considerations need to be made? By developing an understanding of community histories, current contexts, colonial policies, historical events, social determinants of Indigenous health, and the foundational principles for relationship building with First Nations communities, meaningful partnerships may be cultivated with stakeholders and organizations in Indigenous communities across the province.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".