La circularité du questionnement et des questions dans l’entrevue circulaire
Bibliographic record
Abstract
Cet article présente une technique d'intervention très efficace en approche de court terme L'auteure situe d'abord le cadre théorique du modèle d'intervention élaboré par l'Équipe de milan dans lequel l'entrevue circulaire a été instaurée, puis elle rappelle les conditions sous-jacentes à la formulation des questions circulaires, soit la positions méta et la centration sur le processus. Vient ensuite l'explication d'un tableau synthèse reproduit à partir de deux diagrammes conçus par Karl Tomm. On y présente, de façon détaillée, tous les types de questions qui peuvent être formulées dans le cadre d'une entrevue circulaire, les différentes intentions que peut avoir le thérapeute en posant telles questions ainsi que les effets que chaque type de questions suscite chez le système-client et chez le système-intervenant.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".