Bibliographic record
Abstract
Canada is the only developed country with a universal healthcare system that does not cover prescription medication outside the hospital setting (Morgan, Martin, Gagnon, Mintzes, Daw & Lexchin, 2015). Canadians pay for their prescription medications either through private insurance programs or out of pocket (O’Grady, n.d.; Statistics Canada, 2016). As well, many private insurance programs pay a fixed price or percentage, which may still leave people with high out-of-pocket costs (Luiza et al., 2015); especially those needing cancer symptom management medications. Some people, including patients with cancer, report not filling prescription medications, not renewing medications or skipping doses to make the prescription last longer due to financial barriers (Angus Reid Institute, 2015; Briesacher, Gurwitz, & Soumerai, 2007). This can be defined as cost-related nonadherence, a common issue in Canada. In fact, one in 10 Canadians experience cost-related nonadherence (Morgan et al., 2015). Nonadherence to prescription medications is related to poorer health outcomes and an increased use of the healthcare system (Morgan & Lee, 2017). This paper will discuss the current policies and programs for pharmacare in Ontario, and propose solutions that oncology nurses can use to address cost-related nonadherence.
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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".