Framing access to medicines in developing countries: an analysis of media coverage of Canada's Access to Medicines Regime
Bibliographic record
Abstract
BACKGROUND: In September 2003, the Canadian government committed to developing legislation that would facilitate greater access to affordable medicines for developing countries. Over the course of eight months, the legislation, now known as Canada's Access to Medicines Regime (CAMR), went through a controversial policy development process and the newspaper media was one of the major venues in which the policy debates took place. The purpose of this study was to examine how the media framed CAMR to determine how policy goals were conceptualized, which stakeholder interests controlled the public debate and how these variables related to the public policy process. METHODS: We conducted a qualitative content analysis of newspaper coverage of the CAMR policy and implementation process from 2003-2008. The primary theoretical framework for this study was framing theory. A total of 90 articles from 11 Canadian newspapers were selected for inclusion in our analysis. A team of four researchers coded the articles for themes relating to access to medicines and which stakeholders' voice figured more prominently on each issue. Stakeholders examined included: the research-based industry, the generic industry, civil society, the Canadian government, and developing country representatives. RESULTS: The most frequently mentioned themes across all documents were the issues of drug affordability, intellectual property, trade agreements and obligations, and development. Issues such as human rights, pharmaceutical innovation, and economic competitiveness got little media representation. Civil society dominated the media contents, followed far behind by the Canadian government, the research-based and generic pharmaceutical industries. Developing country representatives were hardly represented in the media. CONCLUSIONS: Media framing obscured the discussion of some of the underlying policy goals in this case and failed to highlight issues which are now significant barriers to the use of the legislation. Using the media to engage the public in more in-depth exploration of the policy issues at stake may contribute to a more informed policy development process. The media can be an effective channel for those stakeholders with a weaker voice in policy deliberations to raise public attention to particular issues; however, the political and institutional context must be taken into account as it may outweigh media framing effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".