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Prevalence of alexithymia and its association with burn out among medical field students at Umm Al-Qura University in Saudi Arabia

2022· article· en· W4310864548 on OpenAlexaboutno aff
Abdullah Ahmed Khafagy, Dai Osama Zafer, ‏Nawras Ali Alyamani, Warif Jameel Abdulhaq, Rania Othman Almalayo, Jamila Kamal Asiri

Bibliographic record

VenueMedical Science · 2022
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersUmm Al-Qura University
KeywordsAlexithymiaAssociation (psychology)MedicinePsychologyClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Background/Aim: Alexithymia is the sub clinical inability to recognize and describe one's feelings. In the medical field, students often become emotionally blind when dealing with the difficulties of their studies, which can lead to burnout syndrome. This study's purpose is to assess prevalence of alexithymia and its association with burnout among medical field students at Umm Al-Qura University (UQU), Saudi Arabia. Methodology: A cross sectional survey of medical field students at UQU was conducted to assess the prevalence of alexithymia using the Toronto Alexithymia Scale and academic burnout, measured using the Maslach Burnout Inventory. T-tests were run to assess specialties and gender differences. Result: A total of 387 medical field students completed the study questionnaire. The prevalence of alexithymia among medical field students was 36.7% and alexithymia was detected among 42.2% of female students compared with 30.4% of male students (P=0.033). A significant relation was found between alexithymia among medical field students at UQU and their burnout (P=0.001). Conclusions: One third of the study participants may have alexithymia. Given the important association between burnout and alexithymia, increasing awareness of alexithymia and providing self improvement programs for burnout prevention is highly recommended.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.304
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.

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
Published2022
Admission routes1
Has abstractyes

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