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Record W4288063024 · doi:10.24911/ijmdc.51-1653578655

Public knowledge and attitude regarding epileptic seizure in the Eastern Province, Saudi Arabia

2022· article· en· W4288063024 on OpenAlexaboutno aff
Nasser Almulhim, Khurayzan Binsifran, Fahad A Al Mulhim, Mohammed Al Molhim, Razan Alhussain, Ahmed Almuthaffar, Ahlam Alhussain

Bibliographic record

VenueInternational Journal of Medicine in Developing Countries · 2022
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyMedicineCross-sectional studyQuarter (Canadian coin)Family medicinePositive attitudePsychiatryPsychologyGeography

Abstract

fetched live from OpenAlex

Background: Epilepsy is a neurological condition marked by unpredictably disrupted brain activity known as epileptic seizures. In Saudi Arabia, the prevalence of epilepsy is 6.5 per 1,000. The Saudi community has a low level of awareness regarding epilepsy. Objective: To assess the awareness, knowledge, and attitudes regarding epilepsy in the Eastern region of Saudi Arabia. Methods: This cross-sectional study was conducted among people who lived in the Eastern Province of Saudi Arabia. Data were collected through a self-administered questionnaire that was distributed via social media. Results: A total of 448 responses were received. Participants fulfilling the inclusion criteria completed the study questionnaire. Only 111 (24.8%) participants had good knowledge level regarding epilepsy and its treatment. Good attitude was detected among 50.3% of female participants as compared to 43.1% of males (p = 0.049). Also, 49.5% of participants who had heard or read about disease had good attitude towards epilepsy. Conclusion: Only one-quarter of the participants in the present survey had sufficient level of knowledge about epilepsy and its treatment.

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)0.001
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 score0.271
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.342
Teacher spread0.295 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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