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Record W2900503505 · doi:10.1016/j.phoj.2018.11.186

Interventions to improve the aftercare of survivors of childhood cancer: A systematic review

2018· review· en· W2900503505 on OpenAlexaff
Devonne Ryan, Roger Chafe, Kathleen Hodgkinson, Kevin Chan, Katherine Stringer, Paul Moorehead

Bibliographic record

VenuePediatric Hematology Oncology Journal · 2018
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsSt. John’s Health Sciences CentreJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
Fundersnot available
KeywordsPsychological interventionPsychosocialMedicineIntervention (counseling)Inclusion (mineral)Systematic reviewMEDLINEChildhood cancerGerontologyFamily medicineCancerPsychiatryPsychology

Abstract

fetched live from OpenAlex

This systematic review summarizes the evidence for the effectiveness of interventions aimed at improving the experiences and outcomes for childhood cancer survivors (CCS). We performed a structured literature search of PubMed, EMBASE, CINHAL, ERIC, and PsychoInfo from 1995 to 2017. Studies were included if they (1) described or evaluated a psychosocial, transition, educational, physical activity, or health behavior modification intervention provided to childhood cancer survivors (CCS); (2) presented original empirical research; (3) were published between January 1, 1995 and September 13, 2017; and (4) were full articles, published in English. Twenty-nine articles met our inclusion criteria. The articles covered five main types of interventions: social skills development, physical activity, workbooks, education, and web-based interventions. Study participants found that overall interventions were useful and showed potential to improve health behaviors for CCS. Many of the interventions reviewed were helpful to patients and their families; however, most were at a pilot project stage and evidence for their long-term effectiveness was limited across all studies.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0120.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.052
GPT teacher head0.419
Teacher spread0.367 · 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.

Study designSystematic review
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

Citations14
Published2018
Admission routes1
Has abstractyes

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