Longitudinal analysis of quality of life across the trauma spectrum.
Bibliographic record
Abstract
OBJECTIVE: Few longitudinal studies have examined the relationship between trauma exposure and posttraumatic stress disorder (PTSD) in relation to quality of life or have been designed to consider the relationships between trauma, PTSD diagnosis, and quality of life in terms of both global scores and specific domains. This article aims to provide an essential longitudinal examination of the effects of trauma and PTSD diagnosis on global as well as specific domains of quality of life in a Canadian sample to better understand the diagnosis and unveil possible routes of research and successful treatment methods for the future. METHOD: Data were drawn from the initial two waves of the Zone d'étude en épidémiologie sociale et psychiatrique du sud-ouest de Montréal (ZEPSOM), an epidemiological catchment area study based in southwest Montréal (N = 2,433 at Wave 1 and N = 1,823 at Wave 2). PTSD diagnosis and global and subscale scores of quality of life outcomes were established by face-to-face structured interviews using standardized instruments. Outcomes were compared among 3 trauma/PTSD categories and healthy controls. RESULTS: This study extends previous cross-sectional findings within the catchment area by demonstrating that the effects of current PTSD diagnosis on quality of life endure with time. Specifically, the negative impact of current diagnosis of PTSD on Wave 2 quality of life is expressed through its influence on Wave 1 quality of life. Subscale findings are discussed. CONCLUSION: Research needs to focus on understanding more than just global indices of quality of life when it comes to the trauma spectrum. Additional research remains necessary to fully understand these complex relationships over time. (PsycINFO Database Record
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".