Continuity Clinics in Oncology Training Programs in Canada
Bibliographic record
Abstract
PURPOSE: Continuity clinics (ccs) give trainees an opportunity for longitudinal follow-up of a patient cohort. Trainees can function in a semi-autonomous manner and prepare for independent practice. Data about such clinics in Canada are limited. Our objective was to assess the utility of ccs in Canadian oncology training programs. METHODS: Surveys were developed by the authors for medical and radiation oncology program directors (pds) and trainees, to assess the utility of ccs in Canadian oncology training programs.oncology patients, to assess their attitudes toward ccs. The pds were contacted by e-mail, using the Web site of the Canadian Resident Matching Service; the trainees were contacted by e-mail through the pds and their administrative assistants. Surveys were distributed electronically using SurveyMonkey. Patients were approached by staff oncologists during follow-up visits at The Ottawa Hospital Cancer Centre. RESULTS: Completed surveys were received from 33% of trainees and 63% of pds contacted; patient surveys were completed by 95 patients. Participation in a cc was reported by 47% of responding pds and 37% of responding trainees. Among respondents, 80% rated the ccs as "important" or "very important" to training. The biggest challenge identified by trainees and pds was lack of clinic space. Most pds (57%) and trainees (59%) felt that the staff oncologist should review the patient only if the trainee has concerns, but only 37% of patients shared that view (p = 0.0002). However, many patients expressed the desire to participate in trainee education. CONCLUSIONS: Continuity clinics are considered beneficial by pds and trainees. Patients desire more trainee supervision than the trainees themselves and the pds do, a factor that should be considered when implementing a cc.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".