Preliminary Reliability and Validity of the DMRS-SR-30, a Novel Self-Report Measure Based on the Defense Mechanisms Rating Scales
Bibliographic record
Abstract
Defense mechanisms are psychological factors that influence emotional distress and quality of life. There are a number of measures assessing the construct of defense mechanisms, but only few available instruments reflect the gold-standard theoretical hierarchical organization of defenses. We report on the development of a novel 30 item self-report questionnaire, the DMRS-SR-30, based on the parent instrument, the Defense Mechanism Rating Scales (DMRS). This study tested preliminary reliability and validity of the Italian version of the DMRS-SR-30. We first extracted 30 items from the DMRS Q-sort version (DMRS-Q) and adapted them for a self-reported format. We then applied the DMRS quantitative scoring algorithms to provide proportional scores for the 28 individual defenses and summary scores for seven defense levels and overall defensive functioning (ODF) scores. A dynamic interview was used for assessing participant's defense mechanisms with the observer-rated DMRS and DMRS-Q. We examined internal consistency of the scales along with criterion, concurrent, convergent and discriminant validity among participants (N = 94) who completed the DMRS-SR-30, SCL-90, BDI, and IES-R. Results showed very good internal consistency for ODF (Cronbach's alpha = .890) and the high adaptive defense level, whereas some subscales with few items had lower values. Correlation analyses between DMRS-SR-30 and the two DMRS-based observer-rated measures showed very good criterion and concurrent validity for ODF and moderate to high for defense levels subscales. Correlations between the DMRS-SR-30 ODF and SCL-90 GSI, BDI and IES=R (r = -.456, r= -.540, r = -.402, respectively, all p <.001), indicated good convergent validity. Despite the well-known limitations of self-report methods of psychodynamic phenomena, self-report measures are highly practicable for assessing large samples. The DMRS-SR-30 is the first self-assessed measure describing the whole hierarchy of 28 defense mechanisms and providing scores for ODF, defensive categories, defense levels, and individual defenses. Preliminary examination of the Italian version of the DMRS-SR-30 showed promising results of internal consistency, criterion and concurrent validity, and convergent validity and of the measure. Further validation is needed to confirm these findings and explore other aspects of validity and reliability.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".