Bibliographic record
Abstract
BACKGROUND: Until now, most existentially focused cancer research has been conducted within adult populations. Only a handful of qualitative investigations have captured the experiences of children with cancer relative to themes such as existential fear and finitude, meaning/meaninglessness, uncertainty, authenticity, and inauthenticity. OBJECTIVE: This article aimed to provide a deeper understanding of the existential challenges faced by children living with cancer. METHODS: An interpretive, descriptive qualitative research approach was used. Thirteen children (8-17 years) undergoing treatment for cancer participated. Children participated in individual open-ended interviews and also had the opportunity to journal their experiences in a computerized drawing tool. RESULTS: The 4 main themes that emerged in relation to the existential challenges experienced by children with cancer included (1) existential worry, (2) existential vacuum, (3) existential longing, and (4) existential growth. The drawing tool within the computer diary was found to be particularly beneficial in assisting children to express the existential challenges that they had previously been unable to articulate in words. CONCLUSION: Children moved between existential anxiety and existential growth within the cancer world. The expressive means of drawing pictures gave children a therapeutic space to explore and work at understanding the existential challenges experienced. IMPLICATIONS FOR PRACTICE: This research provides evidence that the active engagement of children's imaginations through the use of a computer-drawing tool may have significant therapeutic value for children with cancer. As well, the findings support the importance of nurses "being there" for young patients with cancer in their time of despair.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".