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Record W2539447787

Predicting Patterns of Global Variations in Electroconvulsive Therapy Utilization

2015· article· en· W2539447787 on OpenAlexaff
Uros Rakita

Bibliographic record

VenueGlobal Health: Annual Review · 2015
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectroconvulsive therapyMental illnessPsychiatrySocioeconomic statusDepression (economics)Mental healthMedicineGovernment (linguistics)Stigma (botany)PsychologySchizophrenia (object-oriented programming)Environmental healthPopulation
DOInot available

Abstract

fetched live from OpenAlex

Neuropsychiatric disorders, primarily depression, represent about 14% of the overall global burden of disease (4). Despite overwhelming evidence that electroconvulsive therapy (ECT) is not only safe but is effective in alleviating many burdensome psychiatric symptoms and treating illnesses, there are notable differences in the rate at which it is used globally (2,3). The current project investigated the extent to which ECT usage rates differ worldwide and possible reasons for this difference. Results indicate higher ECT utilization is independently associated with greater availability of psychiatrists and higher levels of social progress. However, step-wise multiple regression model indicated that government expenditure on mental health explains the majority of variation in ECT usage rates worldwide (R 2 =0.33, p=0.006). Societal stigma against mental illness may also play a role. Both socioeconomic and political factors influence ECT utilization. Efforts to improve the accessibility of ECT from a global perspective may need to address the three-variable model.

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)0.000
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: Review · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.064
GPT teacher head0.421
Teacher spread0.357 · 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
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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