Posttraumatic growth in out-of-hospital cardiac arrest survivors: prevalence and associated factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aims While traumatic experiences can be distressing, they may also foster psychological growth, a phenomenon known as post-traumatic growth (PTG). The aims were to determine 1) the prevalence of PTG, and 2) the influence of survivor characteristics during hospitalization on levels of PTG at follow-up in a Danish cohort of out-of-hospital cardiac arrest (OHCA) survivors. Methods A multicenter prospective cohort study including OHCA survivors, exploring soci-odemographic, clinical, and psychosocial characteristics using the Montreal Cognitive Assess-ment (MoCA), the Hospital Anxiety and Depression Scale (HADS), the Impact of Event Scale-Revised (IES-R), and the Crisis Support Scale (CSS) during hospitalization. At three-month follow-up, structured interviews were conducted to assess PTG at personal, relational, and institutional levels. The influence of survivor characteristics on PTG was explored using Pearson’s chi-square tests. Results Overall, 173 survivors were included. At follow-up, 87% of survivors reported hav-ing one or more levels of PTG. The analysis revealed that the absence of cognitive impairment (MoCA ≥26 vs. MoCA <26) was associated with personal growth (p= .02), being younger (<58 years vs. ≥58 years) with relational growth (p= .03) and being female or having symp-toms of depression (HADS ≥8 vs. HADS<8), with institutional growth (p= .02 and p= .04), respectively. Conclusion The OHCA survivors reported high levels of PTG at three-month follow-up. The type of PTG level was influenced by the absence of cognitive impairment, younger age, fe-male sex, and symptoms of depression during hospitalisation. Social support, symptoms of anxiety, and traumatic distress did not significantly influence the level of PTG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it