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Record W1980400941 · doi:10.1177/1367493506062551

The costs children incur during treatment for cancer: a neglected area in theory and research

2006· review· en· W1980400941 on OpenAlexaff
Argerie Tsimicalis, Patricia McKeever, T. Kavanagh, Bonnie Stevens

Bibliographic record

VenueJournal of Child Health Care · 2006
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsChildhood cancerWork (physics)Cancer treatmentCancerCost–benefit analysisEconomic costIndirect costsMedicinePsychologyPublic economicsEconomicsPolitical scienceMicroeconomics

Abstract

fetched live from OpenAlex

This article argues that the costs children incur during treatment for cancer should be rendered visible. As economic agents, children expend time, engage in productive work and participate in activities. Each of these aspects of daily life can be affected by cancer and/or its therapy and can be considered costs of illness. This article challenges researchers to consider eliciting children's views and assessments of these costs to illustrate more fully the impact of cancer on children and their childhoods. The resulting findings will provide a more accurate account of the social and economic costs incurred by families over the course of children's treatment trajectories. Ultimately, such information could be used to lobby for changes which would improve the life circumstances of children and their families.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.005
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.479
Teacher spread0.408 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2006
Admission routes1
Has abstractyes

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