Study of Defensive Methods and Mechanisms in Developmental, Emotional (Internalization), and Disruptive Behavior (Externalization) Disorders
Bibliographic record
Abstract
We need to find a way for adaptation with inherent unpleasantness of being human condition and conflicts that it caused, as we did not fail. Methods that we used for adaptation are named defense. This research have performed with the aim of study and compare defensive mechanisms and methods of Developmental, Emotional (Internalization), and Disruptive behavior (Externalization) disorders. Method, sample of this research included 390 family that are by available sampling method are selected. Tools of research were structured clinical interview of forth cognitive and statistical guide of psychopathic disorders for axis I and the way used for assess defensive mechanisms is defensive method 40 question's questionnaires of Andrews (1993). The data are compared by statistical methods comparison of averages and one way variance analysis and HSD tests and results show that undeveloped defensive mechanisms in by developmental disorder family (25.2 ± 3.7) mean and standard deviation, it is most used mechanism and in disruptive behavior disorder family by (11.2 ± 1.9) mean and standard deviation is used least mechanism and in developed mechanism of emotional disorder family by (7.8 ± 3.1) mean and standard deviation is most used mechanism and in developmental disorder family by (4.3 ± 1.5) mean and standard deviation is least mechanism in neuroticism patient, social phobia affected emotional disorder family (15.6 ± 2.6) and disruptive behavior disorder family have least mean and standard deviation (9.2 ± 1.7) (p< 0.005). Recent research shows significant of study defensive mechanism in psychopathic family of disorder children that affecting on the way of life of persons and interpersonal and intrapersonal relations and method of solving problem in family of them in life, so defensive mechanisms require more attention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".