Le thérapeute conjugal est un cheval de Troie : Réflexions inspirées des résultats de recherches sur l’intervention auprès des couples
Bibliographic record
Abstract
Communication training and positive exchange training are the two methods for improving marital relations that research has established as being the most effective. However, even after conducting therapy involving both partners based on these methods, too few couples (35%) succeed in reaching the same level of satisfaction as couples who are already satisfied with their relationship. The efforts that have been undertaken to increase this rate have focused mainly on developing new therapeutic techniques that include cognitive, emotional and systemic approaches. However, the results of experimental studies to date do not prove the superiority of these new approaches. Given this situation, it is time to pay more attention to particular characteristics of the therapeutic relation in marital therapy and to the means of obtaining the collaboration of the two spouses. As is the opinion of certain authors, the most difficult clinical task does not consist of finding what the clients must do to solve their problems, but rather to determine how to motivate them and help them achieve their goal. An analysis of this task, based on clinical observations and results of recent research in this area, has allowed the authors to present a number of hypotheses about ways to reinforce the therapeutic alliance and eventually reduce the failure rate of marital therapy.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".