Assessment of home environment for autistic individuals: a literature review of the existing tools
Bibliographic record
Abstract
Background: Autism Spectrum Disorder is a neurodevelopmental disorder, and its prevalence is estimated at 1% worldwide. The home environment can influence activities and roles for autistic individuals, however there are limited assessments that focus on the home environment for this population. Purpose: To identify existing assessment tools of home environment for autistic individuals in the literature. Methods: We explored five databases. Initial search on databases was made in 2019 and updated in 2022. Documents selection was made in two phases: 1) title and abstract screening, 2) full-text reading of selected publications. The included studies were analyzed. Results: We identified seven home environment-related assessment tools that can be used with the autistic population. Most of the tools included few items related to the non-human environment and do not include specific elements of the environment (e.g.: details about sensory stimuli or assessment of the layout). Six of them are design for children or youths and only one out seven is specific to autistic people. Conclusions: The identified tools do not allow for a detailed assessment of the non-human environment of autistic individuals only the identification of environment-related difficulties. More research and the development of new tools are needed to improve it.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".