Towards Health Equity in a National Autism Strategy: A Lens on Disparities, Barriers, and Solutions
Bibliographic record
Abstract
Health equity allows people to reach their full health potential and access and receive care that is fair and suitable to them and their needs regardless of where they live, what they have, or who they are. To achieve health equity, equity in healthcare focuses on the role of the health system to provide timely and appropriate care. When viewed in the context of a National Autism Strategy, this extends to ensuring access to the resources that each Autistic person requires to meet their health needs, such as an autism diagnosis, services, and supports. Based on the equity panel discussion held at the Canadian Autism Leadership Summit 2020, this article reflects on the current disparities and barriers to achieving health equity in a National Autism Strategy, and outlines ways to address them. Disparities to equitable care within the autism community extend from the level of support needs of an individual to how those intersect with several key determinants of health including: geography, culture, gender, and socioeconomic status. Notably, barriers arise due to a “lack of” theme, including lack of awareness, knowledge, access, and voice. Four reoccurring ideas were identified for how to address inequities in health care for Autistic people. First, allocate resources for regional or in-community endeavours; second, improve Autistic representation and connection; third, establish a community of allies to advocate and collaborate; and fourth, establish leadership within the community and government to make disability a priority for Canada. To achieve equity in health care in a National Autism Strategy, we need to look at the intersectionality of autism with the key determinants of health. Moreover, to effectively engage with the government, health professionals, and the public, the autism community should strive to find a unified and diverse voice. And finally, conversation must turn to action.
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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 | 0.034 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.021 | 0.076 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".