Waiting Lists for Radiation Therapy: A Case Study
Bibliographic record
Abstract
BACKGROUND: Why waiting lists arise and how to address them remains unclear, and an improved understanding of these waiting list "dynamics" could lead to better management. The purpose of this study is to understand how the current shortage in radiation therapy in Ontario developed; the implications of prolonged waits; who is held accountable for managing such delays; and short, intermediate, and long-term solutions. METHODS: A case study of the radiation therapy shortage in 1998-99 at Princess Margaret Hospital, Toronto, Ontario, Canada. Relevant documents were collected; semi-structured, face-to-face interviews with ten administrators, health care workers, and patients were conducted, audio-taped and transcribed; and relevant meetings were observed. RESULTS: The radiation therapy shortage arose from a complex interplay of factors including: rising cancer incidence rates; broadening indications for radiation therapy; human resources management issues; government funding decisions; and responsiveness to previous planning recommendations. Implications of delays include poorer cancer control rates; patient suffering; and strained doctor-patient relationships. An incompatible relationship exists between moral responsibility, borne by government, and legal liability, borne by physicians. Short-term solutions include re-referral to centers with available resources; long-term solutions include training and recruiting health care workers, improving workload standards, increasing compensation, and making changes to the funding formula. CONCLUSION: Human resource planning plays a critical role in the causes and solutions of waiting lists. Waiting lists have harsh implications for patients. Accountability relationships require realignment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".