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Record W4409967661 · doi:10.5334/cie.137

A Comparative Analysis of the Protection of the Rights of Childhood Cancer Survivors to Education Under Special Education Law

2025· article· en· W4409967661 on OpenAlexfundno aff
Margaret Flood, Lisa A. Carey

Bibliographic record

VenueContinuity in Education · 2025
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersGovernment of CanadaAustralian Government
KeywordsSpecial educationLawChildhood cancerPolitical scienceMedicineCancerSociologyPsychology

Abstract

fetched live from OpenAlex

Access to equitable education for children treated for cancer is of growing international concern across education, medicine, and related fields. Neurocognitive late effects of childhood cancer and treatment are well established. This impact on cognition results in difficulties with thinking, learning, peer-relationships, and quality of life. Formalized In-School Supports (ISS) can ameliorate the negative impacts of neurocognitive late effects, yet the literature suggests that children treated for cancer often have difficulty accessing these services. This paper reviews the ISS legislation of eight countries regarding protections offered to children treated for cancer and evidence of access to ISS within the literature. The purpose of this review was to look for common barriers for children treated with cancer accessing educational support through ISS. This review identifies gaps between ISS student-focused disability legislation, and practice to inform positive policy change.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.025
GPT teacher head0.395
Teacher spread0.369 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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