Newspaper Coverage of Autism Treatment in Canada: 10-Year Trends (2004–2013)
Bibliographic record
Abstract
OBJECTIVE: To compare trends in coverage of empirically supported and alternative autism treatments in Canadian newspapers during a 10-year period and to examine whether the portrayal of empirically supported and alternative treatments differed. METHOD: We searched a sample of 10 daily local and national Canadian newspapers using the word autism combined with intervention or treatment in the Proquest Canadian Newsstand and Eureka.cc databases, which yielded a total of 857 articles published between 2004 and 2013. In our subsequent analyses, we only included articles whose main topic was autism and that referred to at least one treatment. We then categorized the 137 remaining articles by treatment and rated whether each treatment category was portrayed in a favourable, unfavourable, or neutral manner. RESULTS: In total, 46% of the articles discussed at least 1 empirically supported treatment, 53% at least 1 alternative treatment, and 12% at least 1 uncategorized treatment. Newspaper articles provided favourable, unfavourable, and neutral portrayals of empirically supported treatments in 75%, 10%, and 16% of cases, respectively. In contrast, alternative treatments were portrayed favourably in 52%, unfavourably in 32%, and neutrally in 16% of cases. Our analyses indicated that empirically supported treatments were portrayed more favourably than alternative treatments (χ(2) = 10.42, df = 2, P = 0.005). CONCLUSIONS: Despite some encouraging trends, our study has shown that researchers and clinicians must continue to clarify misconceptions about autism treatment. Families of people with autism spectrum disorders should be directed toward more reliable and accurate sources of information.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.042 |
| 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) | 0.008 | 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".