An examination of cancer patients’ monthly ‘out-of-pocket’ costs in Ontario, Canada
Bibliographic record
Abstract
Ontario cancer patients' monthly out-of-pocket costs (OOPC) were assessed to determine whether these costs were problematic. A self-administered questionnaire was administered to breast (n = 74), colorectal (n = 70), lung (n = 68) and prostate (n = 70) cancer patients between October 2001 and April 2003. It measured categorical OOPC, which were analysed using linear regression modelling, to determine whether any of a variety of independent variables influenced OOPC. Monthly OOPC (mean, range) were: parking/fares ($47, $0-450), devices ($46, $0-2350), prescription drugs ($45, $0-1400), accommodation ($43, $0-1500), complementary and alternative medicine ($29, $0-5000), vitamins ($25, $0-400), homemaking ($14, $0-1000), family care ($12, $0-1200), homecare ($2, $0-330) and other ($8, $0-250), with the total averaging $213 ($0-5230). Imputed travel mileage costs added $372 ($0-6180). Most patients were well served by the current healthcare programmes. In multivariate analysis, variables influencing several OOPC categories were: tumour site, hospitalization, age, and number of clinic trips. Travel costs proved the most problematic, with patients under 65 years and without insurance more likely to have high OOPC. Education and income were not reliable predictors for high OOPC. Many of these costs were for items not traditionally covered by public healthcare financing systems, raising important issues around defining 'medically necessary' care and the role of government.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".