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Record W1995043011 · doi:10.1503/cjs.023911

Psychiatric lessons learned in Kandahar

2011· article· en· W1995043011 on OpenAlexaffvenueabout
Rakesh Jetly

Bibliographic record

VenueCanadian Journal of Surgery · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsMedicineMental healthMilitary personnelAdaptation (eye)PsychiatryStressorLawPsychology

Abstract

fetched live from OpenAlex

Not since the Korean War have the Canadian Forces engaged in combat missions like those in Afghanistan. Combat, asymmetric warfare, violent insurgency and the constant threat of improvised explosive devices all contribute to the psychological stressors experienced by Canadian soldiers. Mental health teams deployed with the soldiers and provided assessment, treatment and education. Lessons learned included refuting the myth that all psychological disorders would be related to trauma; confirming that most patients do well after exposure to trauma; confirming that treating disorders in a war zone requires flexible and creative adaptation of civilian treatment guidelines; and confirming that in a combat mission mental health practice is not limited to the clinical setting.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0150.002

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 head0.467
GPT teacher head0.448
Teacher spread0.019 · 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 designCase report
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

Citations5
Published2011
Admission routes3
Has abstractyes

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