The Impact of Resilience, Alexithymia and Subjectively Perceived Helplessness of Myocardial Infarction on the Risk of Posttraumatic Stress
Bibliographic record
Abstract
Abstract The aim of this study was to investigate the impact of resilience, alexithymia and the subjectively perceived severity (fear of death, pain intensity, helplessness) of myocardial infarction (MI) on posttraumatic symptom severity (PTSS) after MI. Patients were assessed with the Posttraumatic Diagnostic Scale (PDS), Resilience Scale (RS-11) and Toronto Alexithymia Scale (TAS-20). Subjectively perceived severity of MI was measured with three items on a 10-point Likert scale. To test our hypothesis, we applied Pearson correlations as well as multiple hierarchical linear regression analyses. A higher resilience score was significantly associated with lower (r = − .39, p < .001) PTSS. Higher scores of alexithymia (r = .38, p < .01) and subjectively perceived helplessness (r = .42, p < .001) were associated with higher PTSS. Multiple hierarchical linear regression analyses revealed that resilience, the TAS-20 subscale difficulty identifying feelings (DIF) and especially subjectively perceived helplessness were independent significant predictors for the PTSS, adjusted R2 = .29, F(5, 102) = 9.57, p < .001. Our results suggest that resilience reduces the PTSS whereas alexithymia and subjectively perceived helplessness increase the risk. Especially the subjectively perceived helplessness explains a high degree of variance of PTSS and should be assessed to hindering further mental health burden.
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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 | 0.003 |
| 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) | 0.002 | 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".