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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".