An Assessment of How Targeted Health Policies were used to Influence Access to Radiation Therapy Services in Ontario, Canada between 1997 and 2017
Bibliographic record
Abstract
This research provides a broad understanding of the impact of policy on access to radiation therapy (RT) in Ontario. A suite of policies implemented over 20 years address RT wait times and utilization aspects of access to care. The overarching research question was: “What was the impact of provincial and cancer centre level health policies on access to RT in Ontario between 1997 and 2017?” A case study design with multiple embedded units was used. The case was Cancer Care Ontario (CCO), and the embedded units included four representative regional cancer centres. Methods included a document review, longitudinal quantitative data collection, and key informant interviews. The theoretical underpinning was an extension of Kingdon’s Multiple Streams Framework, which collectively examines the problem, solutions and politics surrounding an issue. The access to RT problem evolved from a wait times issue to a utilization issue. Thirty-seven policies were identified and categorized as: (1) improving existing RT capacity, (2) system planning, (3) performance management, (4) human resources, and (5) building new RT capacities. During the first decade, policies were reactive in design, resulting in short-term wait time improvements. Policies in the second decade were more proactive and strategic, providing longer-term solutions. Building new cancer centres yielded the largest sustainable improvement to RT access. Restructuring CCO’s role to an oversight body, and changes to funding mechanisms were necessary system planning policies that also supported access improvement. Politics was highly influential across all levels, with negative media press, public pressure, local politics, and a healthy economy as factors. The Ontario cancer systems’ Regional Vice President role operated as an effective policy entrepreneur. They were able to adapt provincial policy to implement innovative access solutions locally. This longitudinal study provides useful insights into the evolution of Ontario’s cancer system as a more close-knit community of stakeholders, allowing the approach to policy implementation to mature from more reactive to proactive. Access to care continues to be a government priority, particularly as the world struggles through the COVID-19 pandemic. Learnings from this study can inform policy implementation decision-making surrounding access to RT and more broadly cancer and health services.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".