IMRT utilization in Ontario: qualitative deployment evaluation
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to describe a jurisdiction-wide implementation and evaluation of intensity-modulated radiation therapy (IMRT) in Ontario, Canada, highlighting innovative strategies and lessons learned. DESIGN/METHODOLOGY/APPROACH: To obtain an accurate provincial representation, six cancer centres were chosen (based on their IMRT utilization, geography, population, academic affiliation and size) for an in-depth evaluation. At each cancer centre semi-structured, key informant interviews were conducted with senior administrators. An electronic survey, consisting of 40 questions, was also developed and distributed to all cancer centres in Ontario. FINDINGS: In total, 21 respondents participated in the interviews and a total of 266 electronic surveys were returned. Funding allocation, guidelines and utilization targets, expert coaching and educational activities were identified as effective implementation strategies. The implementation allowed for hands-on training, an exchange of knowledge and expertise and the sharing of responsibility. Future implementation initiatives could be improved by creating stronger avenues for clear, continuing and comprehensive communication at all stages to increase awareness, garner support and encourage participation and encouraging expert-based coaching. IMRT utilization for has increased without affecting wait times or safety (from fiscal year 2008/2009 to 2012/2013 absolute increased change: prostate 46, thyroid 36, head and neck 29, sarcoma 30, and CNS 32 per cent). ORIGINALITY/VALUE: This multifaceted, jurisdiction-wide approach has been successful in implementing guideline recommended IMRT into standard practice. The expert based coaching initiative, in particular presents a novel training approach for those who are implementing complex techniques. This paper will be of interest to those exploring ways to fund, implement and sustain complex and evolving technologies.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".