Reducing the impact of distance on hematopoietic cell therapy patients.
Bibliographic record
Abstract
74 Background: Hematopoietic Cell Therapy (HCT) patients experience unique travel challenges and high out-of-pocket costs due to the highly specialized care required. We conducted a mixed methods study to understand current patient support programs in Ontario and other jurisdictions and a cost analysis to inform the development of recommendations to reduce the impact of remoteness on HCT patients and caregivers. Methods: Qualitative information on patient transportation and accommodation supports was gathered through informal and structured input from fourteen Ontario Regional Cancer Program Directors, Hematologists, Patient and Family Advisory Council and Aboriginal Navigators. An environmental scan of medical travel assistance programs within Ontario and in other jurisdictions was performed. A scoping literature review was conducted of published studies focused on inequities in receipt of cancer care in countries with Universal Health care. HCT patient travel patterns to each of the transplant facilities in Ontario were obtained from analysis of Cancer Care Ontario data holdings. Results: We concluded that travel assistance for cancer patients in Ontario varies considerably across the province, and that Ontario lags behind other jurisdictions in Canada and internationally. The scoping literature review revealed that patients who live far from specialist centres, for some diseases, have later stage at diagnosis, less timely access to specialist care, poorer outcomes, lower patient experience scores, and make treatment decisions based on distance. From the analysis of travel patterns for HCT patients, provincially 4 – 79% of patients travel for HCT based on their location (see table below). Conclusions: This study highlights the need to better support HCT patients in Ontario. As a result, a proposal to support accommodations for HCT patients was developed and approved by the Ontario government for implementation in 2018/19.[Table: see text]
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".