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Record W4282925755 · doi:10.1080/13548506.2022.2078496

Emotional reaction to pain as predictor of depression among selected Nigerians living with sickle cell disease in Ile-Ife

2022· article· en· W4282925755 on OpenAlexaboutno aff
Matthew O. Olasupo, Dare Azeez Fagbenro, Boluwatife Oluwagbamila, Mobolaji Grace Olasupo

Bibliographic record

VenuePsychology Health & Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsNigeriansDepression (economics)Beck Depression InventoryDiseaseMedicinePsychiatryAnalysis of varianceClinical psychologyAnxietyInternal medicine

Abstract

fetched live from OpenAlex

This study examined the predictive role of emotional reaction to pain on depression and investigated if there are significant age differences in depression among people living with sickle cell disease. A cross-sectional design carried out at Obafemi Awolowo University Health Centre (OAUHC) in Osun State, Nigeria, was conveniently used to select 71 respondents (females = 70.4%), with a median age of 19 years (SD = 5.94). Beck Depression Inventory (BDI) and Short-Form McGill Pain Questionnaire (SF-MPQ) were used to collect data from the respondents from 11 January to 15 February 2019. Simple linear regression analysis revealed that emotional reaction to pain significantly predicts depression among individuals living with sickle cell disease (R2 = 0.16, F(1, 69) = 16.70, p < .05)). One-way ANOVA results also showed a significant influence of age on depression (F(2, 68) = 4.439; p <.05)). The study concluded that emotional reaction to pain and age play significant roles in depression among people living with sickle cell disease in the study setting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.297
Teacher spread0.289 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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