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Record W2953672889 · doi:10.1192/bjp.209.6.527a

Authors' reply

2016· letter· en· W2953672889 on OpenAlexaboutno aff
Michel Dückers, Eva Alisic, Chris R. Brewin

Bibliographic record

VenueThe British Journal of Psychiatry · 2016
Typeletter
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsnot available
Fundersnot available
KeywordsContent (measure theory)Computer scienceMathematics

Abstract

fetched live from OpenAlex

Is there a vulnerability paradox in PTSD?Pitfalls in cross-national comparisons of epidemiological data Du ¨ckers et al 1 analyse the relationship between prevalence estimates of trauma exposure and post-traumatic stress disorder (PTSD) in published data-sets from 24 countries, and between PTSD and vulnerability (based on a country vulnerability index developed in the 2013 World Risk report).The findings are substantially counterintuitive; countries with low vulnerability have higher lifetime rates of PTSD, meaning that countries with low vulnerability and high trauma exposure have the highest rates of lifetime PTSD.The authors do emphasise a number of limitations of their work, and yet they conclude that a 'vulnerability paradox' exists for both PTSD and depression, with rates higher in countries with more resources and better healthcare systems.This conclusion would seem inconsistent with a great deal of work in global mental health, which emphasises the considerable treatment gap in mental health services, with under-diagnosis and under-treatment particularly high in low-and middle-income countries.2 It raises the question of what precisely is being measured by epidemiological studies of common mental disorders in general, and by studies of trauma exposure and PTSD in particular.3 There has been no shortage of critics of psychiatric nosology, including the construct of PTSD: 4 are counterintuitive findings such as those of Du ¨ckers et al valid in some way, or do they underscore the limitations of our current classification systems, and the epidemiological surveys which employ related measures?Consider, for example, the findings cited by Du ¨ckers et al that in South Africa and Lebanon, 73.8% and 68.85% of the population reported exposure to trauma, lower rates than in The Netherlands or Canada.In our view, given the multiple influences that determine self-reported rates of trauma exposure (including those noted by Du ¨ckers et al), comparing such rates across surveys is a matter of 'comparing oranges and apples' .Other data from other sources may legitimately allow comparison of prevalence estimates: for example, the death rate from motor vehicle accidents in South Africa is 25.1 per 100 000 compared with 3.4 in The Netherlands, and there were 35.7 v. 8.9 murders per 100 000 in South Africa v.The Netherlands. 5 Furthermore, rigorous examination of raw data across surveys (which Du ¨ckers et al note that they did not undertake) allows valid conclusions about trauma exposure: for example, that a small number of traumatic events account for a larger proportion of all traumatic event exposure across the world.5 When it comes to PTSD, Du ¨ckers et al note a prevalence of PTSD of 0.0% in Nigeria, 3.4% in Lebanon, and 9.2% in Canada; they emphasise a range of methodological issues that may have contributed to such findings, but nevertheless proceed to their analysis.In our view, the 0.0% prevalence estimate of PTSD in Nigeria should be considered as a single sampling, prone to any Contents & Is there a vulnerability paradox in PTSD?Pitfalls in cross-national comparisons of epidemiological data & Ethnic density -meaning and implications

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)0.007
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.093
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0050.006
Open science0.0040.004
Research integrity0.0240.032
Insufficient payload (model declined to judge)0.0490.026

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 head0.309
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

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