An Analysis of News Media Coverage of Complementary and Alternative Medicine
Bibliographic record
Abstract
BACKGROUND: To examine the accuracy and adequacy of lay media news stories about complementary and alternative medicines and therapies. METHODOLOGY/PRINCIPAL FINDINGS: A descriptive analysis of news stories about complementary and alternative medicine (CAM) in the Australian media using a national medical news monitoring website, mediadoctor.org.au. Each story was rated against 10 criteria by two individuals. Consensus scores of 222 news articles reporting therapeutic claims about complementary medicines posted on mediadoctor.org.au between 1 January 2004 and 1 September 2007 were calculated. The overall rating score for 222 CAM articles was 50% (95% CI 47% to 53%). There was a statistically significant (F = 3.68, p = 0.006) difference in cumulative mean scores according to type of therapy: biologically based practices (54%, 95% CI 50% to 58%); manipulative body based practices (46%, 95% CI 39% to 54%), whole medical systems (45%, 95% CI 32% to 58%), mind body medicine (41%, 95% CI 31% to 50%) and energy medicine (33%, 95% CI 11% to 55%). There was a statistically significant difference in cumulative mean scores (F = 3.72, p = 0.0001) according to the clinical outcome of interest with stories about cancer treatments (62%, 95% CI 54% to 70%) scoring highest and stories about treatments for children's behavioural and mental health concerns scoring lowest (31%, 95% CI 19% to 43%). Significant differences were also found in scores between media outlets. CONCLUSIONS/SIGNIFICANCE: There is substantial variability in news reporting practices about CAM. Overall, although they may be improving, the scores remain generally low. It appears that much of the information the public receives about CAM is inaccurate or incomplete.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".