Defense mechanisms are associated with mental health symptoms across six countries
Bibliographic record
Abstract
Defense mechanisms are adaptative processes that are related to mental health and psychological functioning and may play an important role in adaptation to distress, as well as in mental health interventions. The present study aimed to compare the use of defense mechanisms and their relationship to mental health symptoms across six countries. In a large-scale descriptive study, we collected data from community- based individuals (N=19,860) in the United States, Australia, Canada, Germany, Italy, and the United Kingdom about the use of defense mechanisms and experienced mental health symptoms during the early phase of the pandemic. We found that the use of defense mechanism categories was similar across countries. Moreover, lower defensive functioning, specifically, neurotic and immature defenses were related to experiencing higher distress across countries, whereas mature defenses were generally inversely related to symptoms. Furthermore, these findings were relatively similar across the six countries. Cross-cultural research on defense mechanisms and mental health has important clinical implications. Our results are consistent with the goal of promoting more adaptive defensive functioning to increase psychological well-being and mitigate the detrimental impact of situational stress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".