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Record W4414643842 · doi:10.47385/cedvr.2212.6.2025

Práticas de aventura e suas aproximações com os transtornos mentais

2025· article· pt· W4414643842 on OpenAlexaboutno aff
Cassio Martins, Érik Imil Viana Farani, Ana Luisa Frantarolli Perfeito

Bibliographic record

VenueCongresso de Educação Física de Volta Redonda · 2025
Typearticle
Languagept
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyIntervention (counseling)Subject (documents)Work (physics)Qualitative research

Abstract

fetched live from OpenAlex

O artigo analisa a relação entre Práticas de Aventura (PA) e saúde mental, com foco em ansiedade, depressão e bem-estar psicológico. Fundamentado na Carta de Ottawa (1986) e na VIII Conferência Nacional de Saúde, o estudo adota uma revisão bibliográfica descritiva, contemplando produções apresentadas no Congresso Brasileiro e Internacional de Atividades de Aventura (2006–2025) e artigos nas bases Google Scholar, SciELO e LILACS. Dos 183 trabalhos analisados nos anais, nove atenderam aos critérios; nas bases, apenas dois estudos foram selecionados, todos sobre saúde mental. Os resultados indicam que as PA, ao aliarem desafio físico, cooperação e contato com a natureza, contribuem para a redução de sintomas ansiosos e depressivos, promovem autoconhecimento, autoestima e autonomia emocional. Atividades como escalada, surf e montanhismo são apontadas como experiências que ressignificam medo e tensão, potencializando o desenvolvimento emocional e social. Apesar de evidências positivas, a produção científica nacional ainda é incipiente e carece de metodologias robustas. O estudo conclui defendendo a ampliação de políticas públicas e pesquisas que incorporem as PA como recurso educativo, preventivo e terapêutico na promoção da saúde mental.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0050.004
Scholarly communication0.0100.008
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.018
GPT teacher head0.339
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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