Program evaluation of a pilot mobile developmental outreach clinic for autism spectrum disorder in Ontario
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with increasing prevalence worldwide. Early identification of ASD through developmental screening is critical for early intervention and improved behavioural outcomes in children. However due to long wait times, delays in diagnosis continue to occur, particularly among minority populations who are faced with existing barriers in access to care. A novel Mobile Developmental Outreach Clinic (M-DOC) was implemented to deliver culturally sensitive screening and assessment practices to increase access to developmental health services, reduce wait times in diagnoses, and aid in equitable access to intervention programs among vulnerable populations in Ontario. METHODS: This study applied two evaluation frameworks (process and outcome evaluation) to determine whether the delivery model was implemented as intended, and if the program achieved its targeted goals. A mixed-methods design was undertaken to address the study objectives. RESULTS: Between September 2018-February 2020, M-DOC reached 227 families with developmental health concerns for their child, while successfully targeting the intended population and achieving its goals. The mean age of the child-in-need at intake was 31.6 months (SD 9.9), and 70% of the sample were male. The program's success was attributed to the use of cultural liaisons to break cultural and linguistic barriers, the creation of multiple points of access into the diagnosis pathway, and delivery of educational workshops in local communities to raise awareness and knowledge of autism spectrum disorder. CONCLUSIONS: The findings underscore the need for community-based intervention programs that focus on cultural barriers to accessing health services. The model of delivery of the M-DOC programs highlights the opportunity for other programs to adopt a similar mobile outreach clinic approach as a means to increase access to services, particularly in targeting hard-to-reach and vulnerable populations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".