A multisite evaluation of summer camps for children with cancer and their siblings
Bibliographic record
Abstract
Summer camps for pediatric cancer patients and their families are ubiquitous. However, there is relatively little research, particularly studies including more than one camp, documenting outcomes associated with children's participation in summer camp. The current cross-sectional study used a standardized measure to examine the role of demographic, illness, and camp factors in predicting children's oncology camp-related outcomes. In total, 2,114 children at 19 camps participated. Campers were asked to complete the pediatric camp outcome measure, which assesses camp-specific self-esteem, emotional, physical, and social functioning. Campers reported high levels of emotional, physical, social, and self-esteem functioning. There were differences in functioning based on demographic and illness characteristics, including gender, whether campers/siblings were on or off active cancer treatment, age, and number of prior years attending camp. Results indicated that summer camps can be beneficial for pediatric oncology patients and their siblings, regardless of demographic factors (e.g., gender, treatment status) and camp factors (e.g., whether camp sessions included patients only, siblings only, or both). Future work could advance the oncology summer camp literature by examining other outcomes linked to summer camp attendance, using longitudinal designs, and including comparison groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".