Framing Incremental Expansions to Public Health Insurance Systems: The Case of Canadian Pharmacare
Bibliographic record
Abstract
Canada is the only country in the world to offer universal comprehensive public health insurance that excludes outpatient prescription medicines. Few scholars have attempted to explain this policy puzzle. We study media coverage of prescription drug financing from 1990 to 2010 to elucidate how the policy problem and potential solutions have been framed in media discourse and identify the actors that have dominated media texts. We confirm previous analyses that have revealed the significant role played by policy elites in media coverage of health reform debates. We also find that proposed expansions to public coverage are presented as a financial liability that could "crowd out" the existing (and popular) public insurance program. Within the context of a predominantly public funded system, framing of incremental expansion reorients away from values and toward discourse related to costs--both of the current system and of potential reforms. This may reflect a strategic narrative used by actors to maintain "silos of values" for coverage for prescription medicines versus those for other services. This has significant implications for the motivation for reform among the electorate and politicians alike, and for the extent to which policy developments, if they occurred, would legitimately reflect societal values for health financing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.028 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".