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Record W2803933179 · doi:10.1080/21635781.2018.1459973

A Critical Outlook on Combat-Related PTSD: Review and Case Reports of Guilt and Shame as Drivers for Moral Injury

2018· article· en· W2803933179 on OpenAlexaff
Eric Vermetten, Rakesh Jetly

Bibliographic record

VenueMilitary Behavioral Health · 2018
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsGovernment of CanadaDepartment of National Defence
Fundersnot available
KeywordsMoral injuryShamePsychologyAngerAnxietyConceptualizationSuicidal ideationDistressAffect (linguistics)Mental healthClinical psychologyPoison controlPsychotherapistSuicide preventionSocial psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

The model of posttraumatic stress disorder (PTSD) that has been dominant for many years has focused on fear conditioning and anxiety-related symptoms as main drivers of the pathology. Yet, the fear based conceptualization fails to consider the rules of modern combat, the culture of combatants, operational stressors and the moral dimension. Recently there is renewed interest in moral distress and moral injury with a focus on guilt, shame, and anger. Accumulating evidence suggests a link between transgression of moral values and symptoms of guilt and shame, anger, suicidal ideation, and PTSD in military servicemen and veterans. Although proper assessment is still in its infancy, there is a need to better understand how moral decisions can affect the mental health of military personnel at any point during their careers, including postrelease. The authors illustrate this with three clinical case reports. They conclude with a call for attention to the relation between the incurrence of moral injurious distress, and the role of guilt and shame as drivers for chronicity of PTSD. Identifying and addressing these issues can contribute to therapy adherence, facilitate successful progression, and contribute to healing and moral repair and reduce overall symptoms of PTSD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.121
GPT teacher head0.506
Teacher spread0.385 · 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
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

Citations41
Published2018
Admission routes1
Has abstractyes

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