Family physicians who have focused practices in oncology
Bibliographic record
Abstract
Objective To characterize the demographic characteristics, practice profile, and current work life of general practitioners in oncology (GPOs) for the first time. Design National Web survey performed in March 2011. Setting Canada. Participants Members of the national GPO organization. Respondents were asked to forward the survey to non-member colleagues. Main outcome measures Profile of work as GPOs and in other medical roles, training received, demographic characteristics, and professional satisfaction. Results The response rate was 73.3% for members of the Canadian Association of General Practitioners in Oncology; overall, 120 surveys were completed. Respondents worked in similar proportions in small and larger communities. About 60% of them had participated in formal training programs. Most respondents worked part-time as GPOs and also worked in other medical roles, particularly palliative care, primary care practice, teaching, and hospital work. More GPOs from cities with populations of greater than 100 000 worked solely as GPOs than those from smaller communities ( P = .0057). General practitioners in oncology played a variety of roles in the cancer care system, particularly in systemic therapy, palliative care, inpatient care, and teaching. As a group, more than half of respondents were involved in the care of each of the 11 common cancer types. Overall, 87.8% of respondents worked in outpatient care, 59.1% provided inpatient care, and 33.0% provided on-call services; 92.8% were satisfied with their work as GPOs. Conclusion General practitioners in oncology are involved in all cancer care settings and usually combine this work with other roles, particularly with palliative care in rural Canada. Training is inconsistent but initiatives are under way to address this. Job satisfaction is better than that of Canadian FPs in general. As generalists, FPs bring a valuable skill set to their work as GPOs in the cancer care system.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".