Bridging gaps in cancer care for young adults: A collaborative e-learning initiative for oncology nurses.
Bibliographic record
Abstract
Background: The global incidence of cancer in young adults aged 18 to 39 has risen significantly, presenting unique challenges that permeate many aspects of their lives, from education to careers and finances. Despite expressing a desire for psychological support, many young adults are reluctant to share their concerns with nurses. As a result, these young adults may feel that their unique experience is not properly acknowledged and that their complex needs are not satisfied. Objectives: This project aims to share the experience of developing an e-learning training for continuous nursing education, focusing on enhancing nurses' awareness, knowledge, and support for young adults with hematological cancer through a collaborative approach. Methods: Utilizing verbatim data from co-design workshops, literature reviews, and The Leukemia & Lymphoma Society of Canada (LLSC) podcasts and resources, a multidisciplinary team developed three modules on understanding young adults, exploring psychosocial challenges, and providing effective support. Pedagogical approaches, inspired by Adult Learning Theory, were combined for a comprehensive and engaging learning experience. Findings: The collaborative e-learning initiative provides a tailored educational experience for nurses that addresses young adults' unique needs and challenges. The program's innovation consists of collaboration with patient-partners and community organizations to design, develop and evaluate its content and its structure. The preliminary evaluation highlights the program's strong potential to enhance nurses' awareness in addressing the unique psychosocial needs of young adults.
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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 | 0.008 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".