Spinal cord injury and aging: an exploration of the interrelatedness between key psychosocial factors contributing to the process of resilience
Bibliographic record
Abstract
CONTEXT: Extant literature highlights how many individuals display resilient trajectories following spinal cord injury (SCI), exhibiting positive psychological adjustment. In the absence of a universal definition, it is agreed that resilience is demonstrated when individuals have better-than-projected outcomes when considering the level of adversity experienced. Previous research has focused on traits connected to vulnerability and maladaptive trajectories following SCI rather than the psychosocial factors that contribute to resilience, which can be cultivated over the lifetime. Individuals living with SCI are now aging and have lifespans paralleling that of the broader older adult population. Aging with SCI can result in a sequela of concomitant pathophysiologic conditions and social challenges, which can undermine resiliency. OBJECTIVE: The purpose of the current commentary is to explore some of the psychosocial factors contributing to resilience within the context of aging with SCI. METHODS: Commentary. FINDINGS: Psychosocial factors contributing to resilience within the SCI population include self-efficacy, social supports, and spirituality. However, these factors are complex and their interconnectedness is not well-understood at the intersection of SCI and aging. CONCLUSION: Understanding the complexities of the contributing psychosocial factors can allow for the development of targeted and innovative multi-pronged rehabilitative strategies that can support resilient trajectories across the lifetime. Future research should move towards the inclusion of additional psychosocial factors, adopting longitudinal research designs, and prudently selecting methods.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| 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".