The unfunded costs incurred by patients accessing plastic surgical care in Northern Saskatchewan
Bibliographic record
Abstract
The Canadian health care system was designed to ensure that all Canadian citizens would receive equal access to health care. However, in rural areas of Canada, patients are required to travel long distances and pay significant out-of-pocket expenses to access health care. The present study attempted to quantify the added out-of-pocket costs that rural Saskatchewan residents must pay to receive plastic surgical specialist care compared with urban residents of Saskatoon. A cost analysis was performed to generate a numerical value that would represent a minimum cost for patients travelling from three different locations within the province. The cost analysis performed in the present study approximated that the unfunded costs for common plastic surgical procedures are, at a minimum, 30 times greater for rural patients in La Ronge compared with their urban counterparts in Saskatoon. The fundamental principle of the Canadian health care system is equal access to necessary health care for all Canadians. Despite this, inequalities persist. The present cost-analysis study demonstrated that the unfunded (out-of-pocket) expenses for rural Saskatchewan patients seeking plastic surgical treatment is significantly higher than for their urban counterparts. These unfunded costs represent a significant barrier to health care access in Canada and serve to propagate inequalities in the nation's heath care system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".