Resourcefulness as a Factor in Recovery From Accident Related Trauma Among Polish Immigrants in Canada
Bibliographic record
Abstract
Background Psychopathological reactions are common occurrences after accident related trauma. This study intended to find out whether psychological resources can be seen as a factor preventing a Major Depressive Disorder after trauma. Methods 89 immigrants were examined. The average age was 46.6 years. 61% of the group were married. MVA were 80% and work-related 20%. The level of psychological resources of those with MDD diagnosis was compared to those diagnosed with PTSD, anxiety, adjustment and pain disorders. The measure of resources was the 18 scales of the Resourcefulness for Recovery Inventory. The severity of depression was measured by the Beck Depression Inventory II. Findings On every measure of resources those who responded to the accident with MDD had lower scores that those who did not (p = .0001). Among the most important resources seem to be: Hope and Expectations, Self-Responsibility, Positive Emotions. A further finding is that in the group of RRI scales called Health Promoting Factors those with MDD were especially low on the scale referring to Acceptance of Reality. The severity of depression measured by Beck Depression Inventory II was significantly correlated (r = 0,5) with a low level of Fundamental Values. Conclusion Our findings indicate that an adaptive reaction to MVA or work related accidents is associated with a high level of internal resources which are also needed for recovery from post traumatic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".