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Record W2490641005 · doi:10.1080/07347332.2016.1217963

A multisite evaluation of summer camps for children with cancer and their siblings

2016· article· en· W2490641005 on OpenAlexaff
Yelena P. Wu, Jessica McPhail, Ryan Mooney, Alexandra Martiniuk, Michael D. Amylon

Bibliographic record

VenueJournal of Psychosocial Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of Health
KeywordsSummer campMedicineAttendancePediatric cancerPediatric oncologySiblingClinical psychologyCancerGerontologyPsychologyDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.424
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2016
Admission routes1
Has abstractyes

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