Media's mixed messages on trans fat and sodium reductions during the policy making process
Bibliographic record
Abstract
Abstract Background During the mid-2000's, reductions in the population intake of trans fat and sodium were commitments of the Government of Canada to reduce population risk for chronic disease. During the policy making process, various stakeholders interpreted and applied evidence in a range of ways to suit their interests. Media as a stakeholder has a powerful role in shaping both political will and public opinion. Methods A media analysis was conducted from 2004 - 2013 to access how trans fats and sodium reduction was portrayed in the Canadian media. Qualitative thematic analysis captured key subject areas including political ideology, personal responsibility/freedoms, industry tactics, evidence updates/disputes and health harms. Overall tone was assessed to be neutral, opposed or in favour of policy regulation. The number of quotes from key stakeholders contributed to the tone classification. Quantitative assessments compared the tone between trans fat and sodium cases. Results Trans fat and sodium were portrayed very differently by the media. Preliminary results show that trans fat was depicted as a harmful industry ingredient, used to extend profits at the expense of public health. On the other hand, communications around sodium research and evidence was not consistent. Media often pitted members of the scientific community against each other and reported on new studies that were outliers to the evidence base. Conclusions The media has an important role to play in shaping public perspectives on nutrition issues and related policy development. The media's mixed messages on sodium reduction may have contributed to the lack of political will and government's policy inaction. Public health practitioners and researchers should consider building in knowledge translation strategies into nutrition advocacy campaigns and research releases. Without these important strategies, the media's framing of nutrition issues could be counter-intuitive to public health aims. Key messages Media seems to have played a key factor in the the development of both trans fat and sodium reduction policies in Canada. Framing of nutrition research results and advocacy messages should align with public health policy goals.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".