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Record W7063099189

We are sorry, but we don’t infect: A comparative study - levels of alexithymia, spontaneity and psychological well-being in subjects with and without psoriasis

2010· dissertation· pt· W7063099189 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório do ISPA (Instituto Superior de Psicologia Aplicada) · 2010
Typedissertation
Languagept
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaDepressive symptomsToronto Alexithymia Scale
DOInot available

Abstract

fetched live from OpenAlex

O tema central desta tese é a psoríase, uma doença crónica de pele, incurável, onde os aspectos psicológicos e sociais estão em risco. Investigamos em particular os níveis de alexitimia, espontaneidade e bem-estar psicológico em doentes de psoríase. Na componente teórica, é apresentada uma perspectiva psicossomática da doença. O objectivo deste estudo é perceber que relação existe entre a alexitimia, a espontaneidade e o bem-estar psicológico nos doentes portadores de psoríase, recorrendo aos instrumentos Toronto Alexithymia Scale-20 (TAS-20), Revised Spontaneity Assessment Inventory (SAI-R) e Escala de Bem-Estar Psicológico (EBEP). Pretende-se igualmente estabelecer comparações entre estes doentes e uma população de não portadores da doença. A amostra é constituída por 175 sujeitos, sendo que 112 são portadores de psoríase. Os resultados obtidos permitiram constatar, na amostra, a prevalência significativa de alexitimia nos doentes de psoríase, em comparação com os não doentes, e níveis de espontaneidade e bem-estar psicológico igualmente mais baixos. Obteve-se uma correlação positiva entre os valores obtidos na SAI-R e na EBEP e uma correlação negativa entre a TAS-20 e a SAI-R e entre a TAS-20 e a EBEP.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.030
GPT teacher head0.301
Teacher spread0.270 · 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
Published2010
Admission routes1
Has abstractyes

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