Understanding post-treatment self-management learning needs of young adults diagnosed with hematological cancer: a qualitative study
Bibliographic record
Abstract
PURPOSE: To better support and promote an active role in survivorship, the purpose of this study was to identify and understand the perspective of young adults diagnosed with a hematological cancer in regard to their post-treatment self-management learning needs. METHOD: An interpretative-descriptive study was conducted. Semi-structured individual interviews were carried out with eight young adults (ages 18 to 29 at the end of active treatment), diagnosed with a leukemia or lymphoma, who have not received active treatment for at least one year. Iterative content analysis of interview data was performed. RESULTS: According to participants, it is when young adults experience transitions or feel alone in dealing with post-treatment challenges that they can identify a learning need. Protective factors and precipitating factors related to young adults, their entourage and their care providers (e.g., young adult characteristics, level of support, quality of patient education) can contribute to shape their view of self-management learning needs. For young adults, an underlying motivation guides the identification of attitudes (autonomy, responsibility, acceptance), knowledge (physical and psychosocial challenges, rights to accommodation for disability) or skills (self-assessment, action-planning, self-advocacy) to be gained. CONCLUSIONS: This study supports that young adults need more than information to feel confident in their ability to self-manage post-treatment challenges. Learning to self-manage is therefore a process of personal transformation, fueled by internal motivation, that also benefits from external support through collaboration between young adults, their entourage, and their care providers including oncology nurses.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".