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Record W2044744201 · doi:10.1002/pbc.20060

Adventure therapy for adolescents with cancer

2004· article· en· W2044744201 on OpenAlexaffabout
Bonnie Stevens, Susan Kagan, Janet Yamada, Iris Epstein, Madelyn Beamer, Mario Bilodeau, Sylvain Baruchel

Bibliographic record

VenuePediatric Blood & Cancer · 2004
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversité du Québec à ChicoutimiSeneca PolytechnicUniversity of Toronto
Fundersnot available
KeywordsAdventureMedicineQualitative researchDescriptive researchExploratory researchPerspective (graphical)Quality of life (healthcare)Descriptive statisticsHealth professionalsHealth careNursing

Abstract

fetched live from OpenAlex

The objective of this study was to describe adolescents' with cancer experience in an adventure therapy program from a health related quality of life (HRQL) perspective. A qualitative descriptive research method was used. Eleven adolescents and five health professionals participated in a guided group adventure therapy expedition in a remote area of Canada. The expedition was videotaped and data were collected using an unstructured interview format with both adolescents and health professionals. Emerging themes were identified using a qualitative descriptive exploratory analysis. Four major themes and related sub-themes were generated. The major themes were: developing connections, togetherness, rebuilding self-esteem, and creating memories. Adventure therapy was viewed by the adolescents and health care professionals as a positive experience with multiple benefits. This preliminary research will contribute to an understanding of adolescents' experiences with cancer and provide a basis for future studies evaluating the impact of adventure therapy on HRQL.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2004
Admission routes2
Has abstractyes

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