Place and Posttraumatic Stress Disorder
Bibliographic record
Abstract
Research on traumatic stress has focused largely on individual risk factors. A more thorough understanding of risk factors may require investigation of the contribution of neighborhood context, such as the associations between perceived neighbourhood disorder and social cohesion with reported trauma exposure (yes/no) and posttraumatic stress disorder (PTSD) diagnostic status (past-year PTSD, remitted). To examine these associations, we used a cross-sectional analysis of an epidemiological catchment area survey (N = 2,433). Visible cues, indicating a lack of order and social control in the community (neighbourhood disorder), were associated with increased trauma exposure (adjusted odds ratio [AOR] = 1.21, 95% confidence interval [CI] [1.12, 1.31]). For trauma-exposed individuals, neighbourhood disorder was associated with greater odds of lifetime PTSD (AOR = 1.38, 95% CI [1.10, 1.75]), and the willingness of residents who realize common values to intervene for the common good (social cohesion), was associated with lower likelihood of past-year PTSD (AOR = 0.64, 95% CI [0.42, 0.97]). For participants with a lifetime diagnosis of PTSD (including past-year), increased social cohesion was associated with higher odds of remission (AOR = 2.59, 95% CI [1.55, 4.30]). Environmental contexts play a role in the development and progression of PTSD. As such, traumatic stress outcomes may be better understood through a perspective that integrates individual and contextual risk factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".