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Record W1994424523 · doi:10.4236/psych.2015.65052

Effects of Developmental Abuse and Symptom Suppression among Traumatized Veterans

2015· article· en· W1994424523 on OpenAlexaboutno aff
John Whelan

Bibliographic record

VenuePsychology · 2015
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClinical psychologySexual abusePsychiatryPsychological abusePhysical abuseDissociative Experiences ScaleIntervention (counseling)Poison controlInjury preventionMedicineMedical emergencyCognition

Abstract

fetched live from OpenAlex

While much of the research on military posttraumatic stress disorder (PTSD) focuses on warzone reactions, a burgeoning literature highlights complex relationships between childhood adversity and adult-onset PTSD. However, conceptual efforts to delineate the effects of childhood abuse on treatment trajectories for traumatized military veterans are lacking. This study compared trauma and psychological symptom profiles for developmentally abused and non-abused Canadian Forces (CF) veterans (N = 108) diagnosed with operational PTSD. Subscale scores from the Detailed Assessment of PTSD Scale (DAPS) and the Personality Assessment Inventory (PAI) were submitted to MANOVA. The analysis resulted in a composite variable reflecting’ symptom suppression efforts’ that separated abused veterans (n = 55) from non-abused veterans (n = 53). Post hoc analyses showed significant differences between the abused sub-groups (i.e., physical and sexual abuse [n = 15]; physical abuse only [n = 17]; sexual abuse only [n = 23]) and the non-abused group. Veterans with abuse histories had higher symptom suppression scores, reflecting higher levels of substance abuse, post-traumatic dissociation, interpersonal mistrust, as well as, lower depression and PTSD impairment scores. Implications for clinicians and an alternative intervention for treating traumatized military personnel with histories of developmental abuse are discussed.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.068
GPT teacher head0.396
Teacher spread0.327 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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