Factors Influencing Post-Traumatic Growth in Emerging Adults with Chronic Medical Illness
Bibliographic record
Abstract
According to the Chronic Disease Prevention Alliance of Canada (2017), approximately 60% of Canadian adults suffer from a chronic medical condition. Managing a chronic medical illness provides an opportunity for post-traumatic growth (PTG). PTG is the positive psychological change that develops because of experiencing a trauma or highly stressful event. The current study evaluated a collection of biopsychosocial factors as potential predictors of PTG. Specifically, we hypothesized that physical pain, perceived social support, coping, pain self-efficacy, pain acceptance, and resilience would produce a model that significantly predicts PTG. Both quantitative and qualitative data from undergraduate students aged 18 to 25 was obtained to gain a comprehensive understanding of the factors that contribute to PTG and how those factors interact with the management of chronic illnesses. Five linear regression analyses were conducted, one for each predictor variable, with resilience as a mediator for PTG. Resilience significantly mediated the relationships between social support, pain intensity, and pain self-efficacy and PTG. Adaptive coping directly affected PTG whereas pain acceptance did not predict PTG in this sample. Furthermore, thematic analysis (Braun & Clark, 2021) was used to analyze the qualitative semi-structured interviews. Five themes were generated using thematic analysis from the qualitative data: 1) embracing the “silver-lining”, 2) integration of the condition, 3) things I wish I knew, 4) chronic illness changes social networks, and 5) the ripple effect. Future research needs a more advanced statistical approach (e.g., SEM) to evaluate how the various predictor variables may potentially interact, especially within different severity levels of chronic pain symptoms.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".