A health technology assessment of Lovaas Autism Treatment : the role of evidence in legal, health policy and health care contexts
Bibliographic record
Abstract
In 1998 parents of autistic children launched a Charter of Rights and Freedoms challenge against the Province of BC for failing to fund Lovaas Autism Treatment (LAT) (Auton et al.). Although initially successful, in 2004 the Supreme Court of Canada overturned the lower courts' decisions and rejected the parents' claims for public funding. In addition to the Charter issue, these legal proceedings also highlighted the discourse over judicial policy making and the Courts' interpretation of medical evidence - specifically, the effectiveness of LAT. The use of medical evidence in law had been identified as an issue by both the American Institute of Medicine (IOM) and the Agency for Healthcare Research and Quality (AHRQ). This thesis was designed to expand on the lOM/AHRQ's previous work by investigating the conceptualizations and processes used by law, health policy and health care within the context of the Auton legal proceedings in order to gain an understanding of how each domain seeks, understands and applies evidence. This was accomplished in two parts. First, the legal dimension of a comprehensive health technology assessment (HTA) framework utilized a qualitative grounded theory methodology to examine participant interviews a n d legal documents. This analysis resulted in a conceptual framework of scientific evidence pathways that further defined, contextualized and dimensionalized the phenomena of seeking, understanding, and applying evidence within the three sectors. Grounded theory proved to be an effective approach for exploring the legal context and serves to broaden the scope of evidence HTA researchers can offer. Second, the effectiveness dimension of the HTA framework employed the methods of systematic review and critical appraisal to investigate the current state of knowledge on the effectiveness of LAT. This analysis concluded that there remains a paucity of rigorously designed studies due to ongoing methodological limitations. Overall, the strength of the body of evidence on LAT was poor and did little to advance its effectiveness claims. Together, these two analyses provided an update on the effectiveness of Lovaas Autism Treatment; insight into how the evidence of LAT was handled in the Auton case; and a depiction of how the sectors of law, health policy and health care conceptualize the evidentiary process in general.
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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 | 0.163 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".