Fostering interspeciality learning in cancer survivorship care: Learning suite results.
Bibliographic record
Abstract
e23003 Background: As survivorship provision declines within cancer centres, primary care providers are increasingly entrusted in the follow-up care of cancer survivors. Empowering specialists and primary care providers about survivorship through educational interventions is essential. Interspecialty education is poorly integrated into residency training, which may impede collaboration between different providers in practice. Interspecialty partnership can positively impact patient and resource-use outcomes. The aim of this study was to assess if a cancer survivorship learning suite (LS) impacts attitudes of family medicine, radiation oncology and medical oncology trainees towards interspecialty collaboration in Montreal, Canada. Methods: A survivorship (LS) developed by a Manitoba-based team under the sponsorship of a Canadian Partnership Against Cancer grant held by Cancer Care Ontario was delivered to 49 McGill University family medicine, radiation oncology, and medical oncology trainees. The LS comprised in-person delivery of a 3-hour case-based workshop, presented by a radiation oncologist and a family physician, both experienced in the field of survivorship. An adapted version of the Readiness for Interprofessional Learning Scale (RIPLS) was completed by participants before and after workshop delivery. Statistical analyses included non-parametric (Wilcoxon Signed rank tests) comparisons. Results: Response rate was 63.2%, and included family medicine (65%), radiation oncology (26%), and medical oncology (10%) trainees, respectively. Following the workshop, participants were significantly more likely to agree that interspecialty learning in residency “would help physicians become better team workers”, (Z = 2.7, p < 0.008, n = 31), and “improves relationships between physicians of different specialties in independent practice afterwards”, (Z = 2.6, p < 0.009, n = 31). Participants were also significantly more likely to agree that “shared interspecialty learning < would > increase < their > ability to understand clinical problems”, (Z = 2.8, p < 0.005, n = 31). Conclusions: While much literature has focused on interprofessional collaboration at different levels of education and practice, few studies have assessed interspecialty collaboration of physicians of different specialties. This survivorship LS demonstrated favorable changes in attitudes towards interspecialty learning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".