Perspectives sur l’usage de l’humour en psychothérapie
Bibliographic record
Abstract
Cet article vise à exposer les différentes facettes liées à l’usage de l’humour en thérapie. Nous espérons ainsi favoriser l’émergence d’une démarche réflexive qui guidera le clinicien dans l’utilisation créative de ses propres interventions humoristiques. Un survol historique du sujet ainsi qu’une définition pratique de l’humour thérapeutique seront présentés. Puis, les principales théories sur l’humour seront révisées. Nous énoncerons également les mécanismes d’action qui pourraient expliquer l’efficacité de l’humour dans le cadre d’une thérapie. Les bénéfices et les écueils potentiels de l’usage de l’humour thérapeutique seront ensuite explorés. Nous tenterons aussi de déterminer les principaux facteurs influençant la réceptivité du patient à l’humour, en plus de proposer une typologie fonctionnelle de l’humour. Enfin, nous décrirons quelques formes d’humour souvent utilisées en thérapie, avec quelques vignettes cliniques à l’appui.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".