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Record W2303763601 · doi:10.1177/1059840516635711

Types and Influence of Social Support on School Engagement of Young Survivors of Leukemia

2016· article· en· W2303763601 on OpenAlexaff
Anne‐Marie Tougas, Sylvie Jutras, Marc Bigras

Bibliographic record

VenueThe Journal of School Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversité du Québec à MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsEmotional supportSocial supportPsychologyDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The present study aimed to describe and explore the influence of social support on the school engagement of young survivors of pediatric leukemia. Fifty-three young Quebecers, previously diagnosed and treated for leukemia, completed a questionnaire measuring their school engagement and participated in an interview focusing on the support offered by four groups of relations with regard to school: parents, siblings, friends, and other nonprofessional relations. The interview responses revealed that parents were perceived to be the primary source of informational and emotional support, with support also provided to a lesser extent by friends, siblings, and members of the extended family. Inferential analyses indicated that young survivors report a higher school engagement score when they perceive themselves as receiving support from a greater number of groups of relations, especially from friends or siblings.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.329
Teacher spread0.300 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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