A impulsividade e alexitimia predizem a agressividade em pessoas idosas institucionalizadas?
Bibliographic record
Abstract
Abstract: Introduction: Alexithymia and impulsivity are related and predict aggressiveness in younger adults, especially in forensic contexts. However, little is known about this relationship in older adults, especially in geriatric institutionalized settings, where aggressiveness presents a high prevalence. Thus, we aimed to analyze the impact of impulsivity and alexithymia in institutionalized older adults’ aggressiveness after examining the relationships between these variables. Relevant variables were controlled for in these relations. Methods: Ninety-seven institutionalized participants (60–94 years, 70.1% women, 59.8% nursing homes’ residents) were assessed with the Buss-Perry Aggression Questionnaire-SF, Toronto Alexithymia Scale-20, and Barratt’s Impulsiveness Scale-15. Results: The self-reported level of aggressiveness was low in our sample. Aggressiveness correlated with and was predicted by alexithymia (R2=17.6%; β=0.24, p<.05) and impulsiveness (R2=17.6%; β=0.34, p<.01). Conclusion: Despite the low levels of aggressiveness (potentially explained by levels of medication, more supervision, and more frailty), our findings with institutionalized older adults demonstrate the relevance of alexithymia and impulsiveness for understanding aggressiveness in older adults, adding to previous studies with other types of populations. We provide directions for psychotherapeutic strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".