Training General Practitioners in Oncology: Lessons Learned From a Cross-Sectional Survey of GPOs in Canada
Bibliographic record
Abstract
PURPOSE: Many countries face a significant shortage of medical oncologists. To mitigate this problem, some countries, including Canada, have established training programs for general practitioners in oncology (GPOs), which train family physicians (FPs) in the fundamentals of cancer care. This type of GPO training model may be useful in other countries facing similar challenges. Therefore, Canadian GPOs were surveyed to learn from their experiences and inform the development of similar programs in other countries. METHODS: A survey was designed and administered to Canadian GPOs to understand the methods and outcomes of GPO training and practice in the Canadian context. The survey was active from July 2021 to April 2022. Participants were recruited through personal and provincial networks and an email list provided by the Canadian GPO network. RESULTS: The survey received 37 responses for an estimated response rate of 18%. Although only 38% of respondents indicated that family medicine training sufficiently prepared them to care for patients with cancer, 90% indicated that GPO training did. Clinics with oncologists were found to be the most effective mode of learning, followed by small group learning and online education. Critical knowledge domains and skills most relevant for GPO training were identified as the treatment of side effects, symptom management, palliative care, and breaking bad news. CONCLUSION: Participants in this survey felt that a dedicated GPO training program offered value beyond family medicine residency in preparing providers to adequately care for patients with cancer. GPO training can be done effectively through virtual and hybrid content delivery. Critical knowledge domains and skills identified as the most important in this survey may be valuable for other groups and nations implementing similar training programs to increase their oncology workforce.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".