Bibliographic record
Abstract
While Canada is known as a leader in global healthcare due to our universal medical coverage, there is a large gap in our current program; Canada is the only country in the world with universal health care that excludes prescription coverage. There is extensive evidence supporting the establishment of a national pharmacare program, such as expanded job opportunities and reductions in prescription medication costs nationally, but there are also limitations to such a program. Concerns with overprescribing, reduction in the variety of available medications, and variable support from Canadians, require careful consideration of this issue from all angles when seeking to implement this program. In addition to these concerns, there are various challenges that would arise if such a program were to be implemented. These include the logistics of a national pharmacare program in our country where healthcare is a provincial jurisdiction, the need for a long-term and stable program, national and inter-provincial record-keeping improvements, and increased public costs.Ultimately, an effective national pharmacare program must take into consideration all of these points, and balance the benefits and downfalls noted, to create a system that supports more comprehensive healthcare for Canadians. This research was completed by reviewing peer- reviewed journal articles obtained through the SFU library database, assigned book readings, and through online search of relevant articles and webpages.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".