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Record W7019942520

A impulsividade e alexitimia predizem a agressividade em pessoas idosas institucionalizadas?

2022· article· en· W7019942520 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório do ISPA (Instituto Superior de Psicologia Aplicada) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaImpulsivityAggressionHuman factors and ergonomicsAffect (linguistics)Relevance (law)Personality
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.290
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRepositório do ISPA (Instituto Superior de Psicologia Aplicada)Same topicElder Abuse and NeglectFrench-language works237,207