Initiation of a multidisciplinary summer studentship in palliative and supportive care in oncology
Bibliographic record
Abstract
PURPOSE: The optimal setting for interprofessional education (IPE) for prelicensure health care trainees is unclear, especially in a field as complex and emotionally challenging as oncology. In this article, the authors describe the initiation of the Cross Cancer Institute Multidisciplinary Summer Studentship in Palliative and Supportive Care in Oncology, a 6-week, multidisciplinary team-based clinical placement in supportive care, designed to incorporate features of best practice cooperative learning. METHODS: A steering committee established goals, structure, eligibility criteria, application process, funding, and a consensus approach to instruction and evaluation for the IPE program. Studentship components included mandatory and flexible clinical time, an exploratory investigation, discussion groups, and a presentation. Two senior students per iteration were selected from clinical nutrition, medicine, nursing, occupational therapy, pharmacy, physiotherapy, respiratory therapy, social work, and speech-language pathology applicants. These students completed questionnaires investigating their views of their own and others' professions at baseline, at the end of the rotation, and 6 months after the studentship. RESULTS: Eight students from medicine, clinical nutrition, occupational therapy, physiotherapy, and speech-language pathology have participated to date. At the elective's end, students have described a more positive view of multidisciplinary team practice, with each participating discipline perceived as both more caring and more subservient than at baseline. In general, changes in attitudes were maintained 6 months after completion of the placement. CONCLUSION: This 6-week multidisciplinary placement is feasible, successful, and potentially transferable to other academic settings. The results of this study suggest that even over as short a period as 6 weeks, objective attitudinal and perceptual change is seen.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".