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Record W1997063817 · doi:10.1177/1363461512447927

Strange but common bedfellows: The relationship between humanitarians and the military in developing psychosocial interventions for civilian populations affected by armed conflict

2012· review· en· W1997063817 on OpenAlexaff
Hanna Kienzler, Duncan Pedersen

Bibliographic record

VenueTranscultural Psychiatry · 2012
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
FundersLondon School of Economics and Political Science
KeywordsPsychosocialPoliticsPsychological interventionHumanitarian aidIntervention (counseling)Mental healthPolitical scienceCold warMilitary personnelCriminologyPolitical economyEconomic growthDevelopment economicsPsychologySociologyLawPsychiatry

Abstract

fetched live from OpenAlex

This essay analyses how the relationships between Cold War and post-Cold War politics, military psychiatry, humanitarian aid and mental health interventions in war and post-war contexts have transformed over time. It focuses on the restrictions imposed on humanitarian interventions and aid during the Cold War; the politics leading to the transfer of the PTSD diagnosis and its treatment from the military to civilian populations; humanitarian intervention campaigns in the post-Cold War era; and the development of psychosocial intervention programs and standards of care for civilian populations affected by armed conflict. Viewing these developments in their broader historical, political and social contexts reveals the politics behind mental health interventions conducted in countries and populations affected by warfare. In such militarized contexts, the work of NGOs providing assistance to people suffering from trauma-related health problems is far from neutral as it depends on the support of the military and plays an important role in the shaping of international politics and humanitarian aid programs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.256
GPT teacher head0.446
Teacher spread0.191 · 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
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

Citations16
Published2012
Admission routes1
Has abstractyes

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