Adverse Childhood Experiences of Disabled Children and Youth Resulting from Ableist Judgments and Disablist Treatments: A Scoping Review
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) are negative but preventable experiences within family and social environments. Originally focused on abuse and household dysfunction, ACE indicators now include many social factors, such as social determinants of health and racism. Disabled Children and Youth (DCY) are particularly vulnerable to ACEs, whereby different body/mind characteristics and lived realities influence ACE exposures and their impacts differently. Racism is recognized as an ACE and as a risk factor that increases ACE exposures and worsens outcomes. Ableism, the negative judgments of body/mind differences, and disablism, the systemic discrimination based on such judgments, are often experienced by DCY with the same three linkages to ACEs as racism. The objective of this scoping review was to analyze how the ACE academic literature covers DCY and their experiences of ableism and disablism using keyword frequency and thematic analysis approaches. Only 35 sources (0.11%) analyzed DCY as survivors of ACEs. We found limited to no engagement with ableism, disablism, intersectionality, the Global South, family members and other DCY allies experiencing ACEs, and ACEs caused by the social environment, as well as few linkages to social and policy discourses that aim to make the social environment better. More theoretical and empirical work is needed.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".