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Record W2611259481 · doi:10.18357/jcs.v37i1.15189

Children Coping With Illness: In Their Own Words

2012· article· en· W2611259481 on OpenAlexaffvenue
Maria Gordon

Bibliographic record

VenueJournal of Childhood Studies · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoping (psychology)NegotiationDiseasePsychologyDevelopmental psychologyMedicinePsychiatrySociologyPathology

Abstract

fetched live from OpenAlex

Children are known to attend school with varying types and degrees of ill-nesses. Some illnesses are apparent to the eye, but some are not so obvious. For example, a child with a broken leg, or any form of physical disability, will be readily noticed in school and, for the most part, will get the necessary atten-tion. However, a child who suffers from an illness such as inflammatory bowel disease would not be immediately rec-ognized as having a disabling problem in school, hence, he or she may not receive the needed attention. This paper focuses on the voices of school children with inflammatory bowel disease as they tell poignant stories of strategies they use to cope with the demands and rigor of school. The article reports on a qualitative study in which I conducted interviews with eight elementary school children with inflammatory bowel disease. The children were able to speak candidly and in their own words, describe their experiences at school and the many challenges they face while suffering from the disease. I then identify key coping strategies that these children use to manage the daily demands of school. The article con-cludes by explaining that school chil-dren rely on the strong support they get from their family and friends to navi-gate their way through school dialogue, to provoke negotiation and to transform our taken-for-granted ideas about chil-dren’s learning, as well as its limita-tions and challenges is offered.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.084
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.352
Teacher spread0.319 · 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.

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

Citations4
Published2012
Admission routes2
Has abstractyes

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