Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".