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Record W4406918298 · doi:10.5114/hpr/196640

Positive and negative emotions in patients with Sudeck’s syndrome according to religiosity before and after the disease

2025· article· en· W4406918298 on OpenAlexaff
Iván Montes-Iturrizaga, Renzo Rivera, Mitchell Clark

Bibliographic record

VenueHealth Psychology Report · 2025
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsMount Royal University
Fundersnot available
KeywordsReligiosityDiseasePsychologyClinical psychologyMedicineInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Sudeck's syndrome is a chronic and painful disease that affects a significant number of people. Despite this, it is a disease little researched in general and even less in the field of the psychology of religion. The aim of this study was to analyze the relationship between religiosity and emotions in patients with Sudeck's syndrome. PARTICIPANTS AND PROCEDURE: The sample consisted of 80 people with Sudeck's syndrome, 92.5% of whom were women. The average age of the participants was 41.8 years, with a range of 23 to 60 years. Participants came from fourteen different countries in the Americas and Europe, including Spain (36.3%), Argentina (20%) and Peru (15%). A clinical and sociodemographic data sheet was used, as well as questions aimed at assessing the emotional state of the participants. RESULTS: The results indicate that patients experienced a significant increase in anxiety and sadness after the diagnosis of the disease, while optimism and energy decreased significantly. On the other hand, no differences were found in positive or negative emotions in believing or non-believing patients. CONCLUSIONS: The data suggest that the diagnosis of Sudeck's syndrome has a negative impact on the emotional health of individuals and that this is independent of whether the patient is a believer or non-believer. However, further research is needed to confirm these findings and to explore the underlying mechanisms of this relationship.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.332
Teacher spread0.324 · 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.

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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