Factors Affecting the Implementation of Complex and Evolving Techniques: A Multiple Case Study of Intensity-modulated Radiation Therapy (IMRT) in Ontario.
Bibliographic record
Abstract
Background: Intensity Modulated Radiation Therapy (IMRT) is a method of delivering high-dose radiation to tumours while sparing surrounding healthy tissues. Despite its wide availability IMRT utilization varies across Ontario. The study’s objective was to examine key steps in the implementation process and identify factors that facilitate or impede IMRT implementation. Research Methods: An embedded multiple case study design, utilizing document analysis and key-informant interviews, was employed. Four cancer centres were selected and key-informant interviews were conducted with radiation oncologists, physicists, radiation therapists, and administrators. Results: Eighteen of 21 invited key-informants participated (86% participation rate) providing a range of insights on the factors influencing IMRT implementation. Overall, three cases made progress in the implementation of IMRT, while one case had limited implementation over the same time period. Conclusion: These findings help explain the observed variation in IMRT implementation across Ontario, which is multifaceted and reflects ongoing processes of change and reinvention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".