Frozen in Time, a Focused Review of Autism Prevalence in Canadian Indigenous Communities
Bibliographic record
Abstract
Yvette Hus Department of Rehabilitation Sciences Theralab Research Director Prof. Kakia Petinou, Cyprus University of Technology, Limassol, CyprusCorrespondence: Yvette Hus, Email yhus@videotron.caAbstract: The unprecedented global continuous rise in autism prevalence is often referred to as a Pandemic while its parallel cost increase to society portrays a Tsunami. Autism data originates mostly from industrialized High-Income geopolitical regions in Europe, North America, and Asian regions. Although efforts to determine autism data from regions in Low and Mid-economies are ongoing, prevalence information from geographically remote and economically vulnerable communities within the privileged regions is largely undetermined, as is the case of the Canadian Indigenous communities, the First Nations, Inuit, and Métis highlighted in this focused review. The underlying theoretical approach adopted here is Transcultural Psychiatry with its emphasis on Context including sociopolitical circumstances, considered the gateway to understanding health, illness, and recovery in groups and individuals. Accordingly, the review includes a concise relevant government system description and history of the relations with Indigenous peoples to provide context to present indigenous relations to Canadian government agencies. Scores in these communities face a myriad of survival challenges encompassing meagre health resources and services. Establishing autism prevalence data in these communities are exceedingly difficult due to multiple factors. While prominent among them are their strong ties to traditional approaches to health, illness, and autism conceptualization, the crucial obstacle is Crown and Provincial government authorities’ and agencies’ historically rooted colonial response to the needs of families with autistic members. It embodies a posture of infantilization, an attitude that is “frozen in time” in the approach, practice, accommodations, and services for these families. The review provides the preferred autism terminology, information sources, article flow, and Future Directions, all found in the Introduction’s first paragraphs.Keywords: focused review, autism prevalence, indigenous peoples, infantilization, sociopolitical context, cultural competence
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".