Promotion or education: a content analysis of industry-authored oral health educational materials targeted at acute care nurses
Bibliographic record
Abstract
OBJECTIVES: To assess the nature, quality and independence of scientific evidence provided in support of claims in industry-authored educational materials in oral health. DESIGN: A content analysis of educational materials authored by the four major multinational oral health product manufacturers. SETTING: Acute care settings. PARTICIPANTS: 68 documents focused on oral health or oral care, targeted at acute care clinicians and identified as 'educational' on companies' international websites. MAIN OUTCOME MEASURES: Data were extracted in duplicate for three areas of focus: (a) products referenced in the documents, (b) product-related claims and (c) citations substantiating claims. We assessed claim-citation pairs to determine if information in the citation supported the claim. We analysed the inter-relationships among cited authors and companies using social network analysis. RESULTS: Documents ranged from training videos to posters to brochures to continuing education courses. The majority of educational materials explicitly mentioned a product (59/68, 87%), a branded product (35/68, 51%), and made a product-related claim (55/68, 81%). Among claims accompanied by a citation, citations did not support the majority (91/147, 62%) of claims, largely because citations were unrelated. References used to support claims most often represented lower levels of evidence: only 9% were systematic reviews (7/76) and 13% were randomised controlled trials (10/76). We found a network of 20 authors to account for 37% (n=77/206) of all references in claim-citation pairs; 60% (12/20) of the top 20 cited authors received financial support from one of the four sampled manufacturers. CONCLUSIONS: Resources to support clinicians' ongoing education are scarce. However, caution should be exercised when relying on industry-authored materials to support continuing education for oral health. Evidence of sponsorship bias and reliance on key opinion leaders suggests that industry-authored educational materials have promotional intent and should be regulated as such.
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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 | 0.022 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".