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Record W2140693808 · doi:10.1017/s0033291707000955

Life trajectories and burden of adversity: mapping the developmental profiles of suicide mortality

2007· article· en· W2140693808 on OpenAlexaff
Monique Séguin, Alain Lesage, Gustavo Turecki, Mélanie Bouchard, Nadia Chawky, Nancy Tremblay, France Daigle, Andrée Guy

Bibliographic record

VenuePsychological Medicine · 2007
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalUniversité du Québec en OutaouaisMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsSuicide preventionPoison controlInjury preventionHuman factors and ergonomicsPsychologyOccupational safety and healthMedicineMedical emergencyPsychiatryDevelopmental psychologyDemographySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about differential suicide profiles across the life trajectory. This study introduces the life-course method in suicide research with the aim of refining the longitudinal and cumulative assessment of psychosocial factors by quantifying accumulation of burden over time in order to delineate distinctive pathways of completed suicide. METHOD: The psychological autopsy method was used to obtain third-party information on consecutive suicides. Life-history calendar analysis served to arrive at an adversity score per 5-year segment that was then cluster-analysed and correlated to define victim profiles. RESULTS: Two distinct life trajectories emerged: (1) individuals who experienced childhood traumas, developmental adversity and little protection were more likely to present concurrent psychiatric and Axis II disorders; and (2) individuals who experienced less adversity but seemed more reactive to later major difficulties. CONCLUSIONS: The life calendar approach presented here in suicide research adds to the identification of life events, distal and recent, previously associated with suicide. It also quantifies the burden of adversity over the life course, defining two distinct profiles that could benefit from distinct targeted preventive intervention.

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)0.001
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 score0.036
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.137
GPT teacher head0.377
Teacher spread0.239 · 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

Citations99
Published2007
Admission routes1
Has abstractyes

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