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Record W2991013501 · doi:10.1590/s0103-73312019290317

Luto e medicalização: gestão do sofrimento entre mães que perderam filhos

2019· article· pt· W2991013501 on OpenAlexaff
Aline Ferreira de Faria, K.Lee Lerner

Bibliographic record

VenuePhysis Revista de Saúde Coletiva · 2019
Typearticle
Languagept
FieldPsychology
TopicPsychoanalysis and Psychopathology Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Resumo O artigo coloca em discussão a medicalização como forma de gestão do sofrimento de mães que perderam filhos, evidenciando como o luto é psicologizado e psiquiatrizado na sociedade contemporânea e como os saberes "psi" são apropriados por aqueles que sofrem. O objetivo do trabalho foi compreender como as mães que perderam filhos lidaram com esses saberes na gestão de seu sofrimento. Optou-se metodologicamente pela realização de entrevistas com base em roteiros semiestruturados com mães enlutadas e alguns de seus familiares e amigos, sempre conduzidas a partir de um olhar etnográfico. Ao todo, foram entrevistadas seis mães e seis familiares. Conclui-se que a relação entre a medicalização e o luto é permeada por um constante processo de negociação, pois o saber adquirido pelas mães a partir de suas experiências e o "saber psi" institucionalizado atuam em uma relação de complementaridade.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0900.025

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 head0.342
Teacher spread0.326 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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