Bibliographic record
Abstract
Previous studies investigating the relationship between social capital and posttraumaticstress disorder (PTSD) have yielded mixed results. Although it is well known in theliterature that feeling socially supported by one’s community, family, and friends has apositive impact on mental wellbeing, the effects of social capital specifically with regard toPTSD are not yet clear. A primary reason for this is that many previous studies havefocused on populations in which all individuals experienced the same traumatic event,rather than different events on their own. Further, it is not clear if different components ofsocial capital have different effects on PTSD symptomatology, or if social capital holdsdifferent benefits at different stages of PTSD. The current study aims to examine if eachcomponent of social capital has an influence on the different diagnostic outcomes of PTSD.This was done through an analysis of data collected from an epidemiological catchment areastudy in a community in southwest Montreal (N = 1812). Outcomes of PTSD diagnosis areas follows: (a) Current PTSD, (b) Remitted PTSD, (c) Trauma Exposure, No PTSD, and (d)No Trauma Exposure. These four groups were analysed with regard to four prominent andcore components of social capital: (i) Sense of Collective Efficacy, (ii) NeighbouringBehaviour, (iii) Community Participation, and (iv) Sense of Community. Results revealedsignificant relationships between each trauma category and social capital scale. Theseresults may speak to the importance of support, as well as perception of support at thecommunity-level in coping with PTSD and mental health generally. The present studyprovides an important expansion of the current knowledge of the relationship betweensocial capital and PTSD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".