Case 15 : Going Beyond the Wheel Chair Ramp: Public Health Sudbury & Districts’ Plan to Become Accessible to All
Bibliographic record
Abstract
Following years of advocacy work, Christina Peterson, foundational standards specialist at Public Health Sudbury & Districts (PHSD) and co-chair of the Evidence-Informed Practice Working Group (EIPWG), facilitated the formation of the People with Disabilities Working Group in early 2015. People with disabilities (PWD) faced significant health inequities compared to people without disabilities. The People with Disabilities Working Group had established three long-term outcomes from their logic model: Programs and services at PHSD are fully accessible and inclusive, particularly for people with disabilities (especially for unseen disabilities). Staff at PHSD have the ability to recognize, understand, and apply attitudes and practices that are sensitive to and appropriate for people with disabilities. Staff have the knowledge, attitudes, and skills to ensure programs and services are fully accessible and inclusive for people with disabilities. \nOftentimes, public health programs, services, infrastructure, and policies are not designed with people with disabilities in mind. Healthcare professionals often focus on disabilities alone, rather than the needs of the whole person. PHSD recently developed ten promising local public health practices to reduce social inequities in a health framework. The PWD working group had made some progress towards their long-term goals, such as a board-approved motion for a personcentred language statement within PHSD. However, Christina knew that there was very little done that was based on the health equity framework they had established, especially for those with unseen disabilities. There was a need to go “beyond the wheel chair ramp”.\nThe goal of this case is for students to understand the definition of health equity and recognize its importance when planning, delivering, and evaluating public health programs, services, infrastructure, and policies within agencies. Based on a modern public health issue, students will have the opportunity to apply promising evidence-based public health practices to reduce social inequities in health and devise other programs when dealing with a marginalized population.\nThe backdrop of the case, which depicts Christina’s fight to create change within an organization, will highlight the social-ecological model of behaviour change typically applied in health promotion strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".