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Record W2041573383 · doi:10.1080/13607863.2015.1018864

Post-traumatic stress syndrome in a large sample of older adults: determinants and quality of life

2015· article· en· W2041573383 on OpenAlexaffabout
Catherine Lamoureux‐Lamarche, Helen‐Maria Vasiliadis, Michel Préville, Djamal Berbiche

Bibliographic record

VenueAging & Mental Health · 2015
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsHôpital Charles-Le MoyneUniversité de Sherbrooke
Fundersnot available
KeywordsQuality of life (healthcare)MedicineLogistic regressionMarital statusAnxietyScale (ratio)GerontologyMental healthPsychiatryEnvironmental healthPopulationInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: The aims of this study are to assess in a sample of older adults consulting in primary care practices the determinants and quality of life associated with post-traumatic stress syndrome (PTSS). METHOD: Data used came from a large sample of 1765 community-dwelling older adults who were waiting to receive health services in primary care clinics in the province of Quebec. PTSS was measured with the PTSS scale. Socio-demographic and clinical characteristics were used as potential determinants of PTSS. Quality of life was measured with the EuroQol-5D-3L (EQ-5D-3L) EQ-Visual Analog Scale and the Satisfaction With Your Life Scale. Multivariate logistic and linear regression models were used to study the presence of PTSS and different measures of health-related quality of life and quality of life as a function of study variables. RESULTS: The six-month prevalence of PTSS was 11.0%. PTSS was associated with age, marital status, number of chronic disorders and the presence of an anxiety disorder. PTSS was also associated with the EQ-5D-3L and the Satisfaction with Your Life Scale. CONCLUSION: PTSS is prevalent in patients consulting in primary care practices. Primary care physicians should be aware that PTSS is also associated with a decrease in quality of life, which can further negatively impact health status.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.942

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 studies0.0000.000
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 head0.105
GPT teacher head0.448
Teacher spread0.343 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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