Applying and Extending the Conservation of Resources (COR) Model to Trauma in U.S. Veterans
Bibliographic record
Abstract
This was a novel pilot study about the relationship between PTSD severity and resource gain and loss using the conservation of resources (COR) model with U.S. Veterans. Higher PTSD severity was predicted to be associated with greater resource loss scores, and lower PTSD scores were predicted to be associated with greater resource gain scores. The sample size was limited (N = 19) due to the COVID-19 outbreak. Veterans completed a demographic questionnaire, the Montreal Cognitive Assessment (MoCA), the Combat Exposure Scale (CES), the PTSD Symptom Scale–Interview (PSS-I), the Conservation of Resources–Evaluation (COR-E), and two additional open-ended questions. A statistically significant negative medium effect size was found between PTSD diagnosis and resource gain (r(17) = −0.42, p = 0.039, one-tailed). A large effect size in resource gain scores between PTSD and non-PTSD groups was also found (t(17) = 1.880, p = 0.077, d = 0.87), with the non-PTSD group reporting more gain of resources than the PTSD group. Post hoc tests revealed that the resource gain score of the mild PTSD group was significantly higher than that of the severe + very severe PTSD group (p = 0.034). Results suggest that resource gain, when compared to resource loss, was the strongest predictor for a non-PTSD diagnosis.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".