Bibliographic record
Abstract
Rural residents can incur substantial travel-related costs to receive needed care. In this study, we describe and compare the medical travel programs offered by provincial and territorial governments. We conducted a document analysis of medical travel subsidy programs available in Canada to the general public. Only programs funded and administered by provincial/territorial governments were included. Based on the information that we collected, we determined there were three types of programs. Discount programs (BC) allow eligible patients to receive reduced or waived prices for travel and lodging at designated providers. Non-reimbursement programs (BC, SK) cover the costs of travel and lodging without requiring patients to pay for costs up-front. In reimbursement programs (MB, ON, QC, PEI, NS, NL, YK, NWT, NT), patients generally pay costs up-front and then submit claims for reimbursement after receiving the health service. Rates, co-payments, and maximum allowable amounts vary by program. Our findings indicated that although many provinces and territories offer medical travel subsidy programs, the availability, terms, and conditions vary widely. The study highlights regional disparities that may contribute to inequitable access to care across Canada.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".