L'évaluation normalisée et clinique des mécanismes de défense: Revue critique de 6 outils quantitatifs
Bibliographic record
Abstract
BACKGROUND: The defense mechanisms (DM) concept goes back to the foundation of psychoanalysis and is one of its theoretical cornerstones. Recently, with the introduction of this notion in an experimental item of categorical classification, DMs have become a new field in research and scientific communication. The increasing number of studies taking DMs into account matches the development of clinical evaluation scales that are easier to use than projective tests. To our knowledge, there is no comparative analysis of these tools. OBJECTIVE: We aimed first to describe the operating mode and metrological qualities of the most recent scales and then to highlight the benefits and limitations of these clinical evaluation tools. Finally, this article aims to help clinicians choose a tool that is most convenient for their protocol. METHOD: We introduce the following tools through a literature review: Defense Mechanism Inventory, Defense Mechanism Profile, Defense Style Questionnaire (DSQ), Defense Mechanism Rating Scale, Life Style Index, and Response Evaluation Measure. CONCLUSION: Using clinical scales includes many limitations associated with the DM concept. Nevertheless, their feasibility and validity warrant their use. The DSQ stands out for its many qualities, but the other tools specificities are yet to be considered in regard to the chosen protocols.
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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 | 0.118 | 0.270 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.009 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".