Un regard sur le domaine de la psychothérapie et sur le développement du programme APAP [Exploring the field of psychotherapies and the development of APAP]
Bibliographic record
Abstract
Objective The field of psychotherapy is growing and is offering more and more types of treatments. The objective of this paper is to go through many clinical researches and experiences from psychotherapists with several psychotherapies in universities in Montreal and Lausanne, especially in the Institute for Mental Diseases in Montreal (IUSMM). Method Research and clinical evaluations have varied over many years. Many studies and results have been evaluated and discussed over those years. Clinical developments like APAP have been developed and evaluated in various milieus and applications. Various strategies have been proposed like APAP that can be added to many orientations of psychotherapies. APAP is called for Augmentation de la Psychothérapie par Amorçage Préconscient in French or "Psychotherapy Augmentation through Preconscious Priming" in English. Results Many various researches focusing psychotherapies were developed over long periods because psychotherapists were also clinicians and teachers. For example, we were able de proceed with clinical evaluations and many volunteer patients. We were able to demonstrate that preconscious listening was useful to facilitate cognitive change. Conclusion Research on psychotherapy remains difficult and require usually long periods. For example, the research APAP described here took much more than one year. Research remains also difficult because double blind procedures in research on psychotherapy are rare. We were lucky because preconscious listening was out of conscious listening creating a sort of placebo that was to be compared to the listening of personalized preconscious stimuli.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".