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Record W2075684142 · doi:10.1002/jts.21726

PTSD and depression following armed robbery: Patterns of appearance and impact on absenteeism and use of health care services

2012· article· en· W2075684142 on OpenAlexaff
Geneviève Belleville, André Marchand, Marie‐Hélène St‐Hilaire, Mélissa Martin, Cidália Ferreira Silva

Bibliographic record

VenueJournal of Traumatic Stress · 2012
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsInstitut universitaire en santé mentale de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsAbsenteeismMajor depressive disorderPsychiatryDepression (economics)Mental healthMedicinePopulationOccupational safety and healthSuicide preventionPoison controlPsychologyClinical psychologyMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

Armed robbery is a sudden, life-threatening event affecting the victims' mental health. Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) in this population have rarely been studied. The objectives of the study were to assess the occurrence of PTSD and MDD in victims of armed robbery, and to evaluate occupational functioning and use of health care services. Eighty-six convenience store employees, victims of armed robbery, were evaluated within days after the robbery, and 1 and 3 months after. A validated diagnostic interview (SCID-I) was used. Data about sick leave, absenteeism, and use of health care services were collected. The total number of individuals who had PTSD, MDD, or both at any time during the 3 months following the robbery was 1 (2%), 4 (6%), and 5 (8%), respectively, showing that comorbid PTSD-MDD is as frequent as or even more frequent than either disorder in isolation. Individuals with PTSD (with or without comorbid MDD) reported more absenteeism (η(2) (p) = .25) and more medical visits (η(2) (p) = .12) following the robbery. Clinicians and management resources personnel must be alert to the possibility that both PTSD and MDD, either alone or comorbid, can develop in victims of armed robbery.

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 score0.062
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.071
GPT teacher head0.407
Teacher spread0.336 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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