Meaning-making following spinal cord injury: Individual differences and within-person change.
Bibliographic record
Abstract
OBJECTIVE: Several studies of people coping with trauma or loss suggest that finding meaning in one's experience predicts better adjustment. However, these studies assume that meaning is a stable individual-difference construct. We assess the temporal stability of searching for and finding meaning in a sample of people with spinal cord injury (SCI), and test the effect of change in searching and finding meaning on depressive affect, subjective well-being (SWB), and perceived growth. METHOD: Sixty-seven adults with SCI were interviewed on 3 occasions over the first 13 months of their injury. RESULTS: Searching for and finding meaning are moderately stable over time. Multilevel analyses indicated that in addition to between-person effects of searching for and finding meaning on depressive symptoms and well-being, there was also evidence of within-person effects of searching for and finding meaning, such that more frequent searching was associated declines in adjustment, whereas increases in found meaning were associated with improved adjustment. Finding meaning, at both the between-person and within-person levels, was associated with greater perceived growth, but such growth was not associated with depressive symptoms or SWB. CONCLUSIONS: The within-person analyses demonstrate that meaning-making is a process that is not necessarily linear yet is important for understanding the process of adaptation for many people coping with SCI.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".