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Record W4387499885 · doi:10.61482/biyu.23.01

Impacto del uso de psicoestimulantes (Modafinilo y Metilfenidato) en los estudiantes de medicina en relación con el síndrome de burnout

2023· report· en· W4387499885 on OpenAlexaff
Jorge Fernando Iturribarría Chávez, Milen Montserrat Mendoza Angeles, Rebeca Torres Carrasco, Andrea Romo Jiménez, Maria Monserrath Pacheco Vazquez

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsImpact
Fundersnot available
KeywordsBurnoutMethylphenidateMedical prescriptionBurnout syndromeAnxietyPsychologyPsychiatryDepression (economics)MedicineCynicismClinical psychologyAttention deficit hyperactivity disorderNursingPolitical science

Abstract

fetched live from OpenAlex

Burnout syndrome refers to exhaustion, cynicism and inefficiency. It can occur at work or in an academic environment. The stressful lifestyle of students and the risk of anxiety, depression and sleep disorders due to the high academic demand are risk factors for burnout syndrome. As a result of the high prevalence of burnout syndrome, there is a high risk of the use of psicoestimulantes or known as smart drugs in 13.1%. Among the most consumed psychostimulants without a medical prescription, modafinil and with a medical prescription, methylphenidate. Within the studied sample, 84.5% presented high or very high levels of exhaustion. 80% of the subjects who consume modafinil use it with the purpose to improve their academic performance and increase their concentration, however the drug is indicated in drowsiness, sleep disorders. About 100% of those who consume methylphenidate do so by medical prescription.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.105
GPT teacher head0.474
Teacher spread0.369 · 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
Published2023
Admission routes1
Has abstractyes

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