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Record W4281990775 · doi:10.1002/smi.3172

Peritraumatic distress across the lifespan: Clinical implications of age differences during the COVID‐19 pandemic

2022· article· en· W4281990775 on OpenAlexaff
Romain Hassan Omar, Justine Fortin, Marjolaine Rivest‐Beauregard, Michelle Lonergan, Alain Brunet

Bibliographic record

VenueStress and Health · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of OttawaUniversité de MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDistressLearned helplessnessCoronavirus disease 2019 (COVID-19)PandemicFeelingClinical psychologyPsychologyMedicinePsychiatryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract The Peritraumatic Distress Inventory (PDI) is a well‐known self‐report questionnaire indexing the distress experienced during and shortly after a most stressful or traumatic event. Although sociodemographic factors contributing to peritraumatic distress have been previously investigated, no research has examined the nature and severity of peritraumatic distress reactions in a non‐clinical, community sample as a function of age. An international sample of 5621 adult participants were grouped according the World Health Organization's age stratification protocol. Mean scores and item endorsement on the PDI were compared across groups with respect to their worst experience of the COVID‐19 pandemic. A significant between‐group difference was found, F (55,615) = 30.74, p < 0.001, n 2 = 0.027 whereby participants aged 18–39 years old reported the highest levels of peritraumatic distress. This group also endorsed a higher proportion of items on the PDI's two main factors (emotional distress and physical reactions), and were more likely to endorse feelings of helplessness, than older participants. It appears that severity of peritraumatic distress during the pandemic has affected younger people the most. Results are discussed in light of clinical implications.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.347
GPT teacher head0.541
Teacher spread0.195 · 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
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

Citations3
Published2022
Admission routes1
Has abstractyes

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