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Record W2560787164

MAKING ILLNESS AND HOSPITALISATION BEARABLE: A THEATRE FOR SICK CHILDREN

2008· article· en· W2560787164 on OpenAlexaboutno aff
Ami Rokach

Bibliographic record

VenueArchives of Disease in Childhood · 2008
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSick childLaughterMedicineAngerCourageFace (sociological concept)Trial by ordealPreferenceSick leaveNursingDevelopmental psychologyPediatricsFamily medicineSocial psychologyPsychiatryPsychologyPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Hospitalisation is stressful. It is so for adults and more so for children. The stresses imposed by hospitalisation may precipitate uncharacteristic behaviours and emotions in children, which in turn may become a major source of stress for their parents. Research has demonstrated that humour that provokes laughter has both psychological and physiological effects. Humour in hospitals helps patients and their families deal with anger and other emotions that they may experience. It can also “soften” hospitals that are almost always sterile, impersonal and isolating places. Accordingly, a theatre was erected in Toronto’s Hospital for Sick Children. The play combines story telling, humorous insights and a message for sick children. It aims to empower and encourage them to face their ordeal, in and out of the hospital, with courage and self-acceptance. Children were interviewed, along with their parents before and after they watched the play and were asked why they chose to attend the play, what did they find particularly interesting, which of the characters did they identify with and what did the play mean to them in the light of their illness and hospitalisation. Results indicated that parents derived quite a lot of satisfaction seeing their sick children laugh and cheer the actors. The children themselves indicated a significant preference for the play characters that were able to overcome obstacles and make the best of the situation.

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)0.000
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 score0.115
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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