Bridging the gap: The design of a survivorship curriculum for interspecialty collaboration.
Bibliographic record
Abstract
13 Background: Interspecialty learning between trainees from different postgraduate training programs is unusual in Canada. Primary care providers (PCPs) have an increasing role in the provision of survivorship care in collaboration with cancer specialists (ONC), but coordination of care is often lacking and avenues for joint learning and interaction among these physicians are limited during residency. We are piloting a learning suite (LS) for PCP and ONC trainees in MB, ON & BC as part of a pan-Canadian study on integration of care between primary and cancer specialty care. Methods: Using Kern’s Six-Step Approach to Curriculum Design, a national team of experts conducted surveys and focus groups with postgraduate program directors, cancer survivors, and trainees. We set learning objectives as informed by the needs assessment and used constructive alignment to build the curriculum in a blended learning format: online, workshop and clinical. We are assessing inter-disciplinary learning outcomes comparing pre and post results on a modified Readiness for Interprofessional Learning Scale (M-RIPLS) in three pilot sites in 2015 with about 40 family medicine and oncology trainees. Results: Learning materials have been developed for a mixed audience of trainees. The interactive, one hour online session addresses cancer epidemiology, the domains of survivorship care, and specific issues in follow-up care for three cancer types, as well as province-specific survivorship initiatives. This is followed by a two hour, case-based learning workshop that focuses on collaboration and shared care. A clinical experience in cancer follow-up clinics concludes the LS. In PCP training sites without a nearby cancer centre, trainees are able to review videos of actual transitional appointments and follow-up clinics and of the cancer centre/oncologist perspective on shared care. Pilots are ongoing in 2015 with national rollout in 2016. Conclusions: We expect that learning together in residency will impact on attitudes towards interspecialty collaboration in the care of cancer survivors. This interspecialty, blended learning curriculum will enhance the place of survivorship training in the postgraduate education of Canadian physicians.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".