Hope and psychological health and well-being following spinal cord injury.
Bibliographic record
Abstract
OBJECTIVE: Several studies of people with spinal cord injury (SCI) have indicated that high levels of hope are linked with better adjustment, but none has assessed the extent to which hope predicts change in adjustment over time. This study examines the effect of hope assessed within the first months post-SCI onset on changes in several indicators of well-being just prior to release from institutional care and again 13 months post-SCI. METHOD: Structured interviews were conducted with 67 adults (54 men, 13 women; Mage = 44.7 years, SD = 17.2) with SCI on average 2.6 months (Time 1), 5 months (Time 2; n = 60), and 13 months post-SCI (Time 3; n = 53) using validated instruments to assess dispositional hope, depressive symptoms, subjective well-being, self-esteem, reintegration, and pain. RESULTS: Regression analyses revealed that, of the five indicators of well-being, hope at Time 1 only significantly predicted increases in subjective well-being at Time 2. However, hope predicted increased well-being on 4 of 5 indicators at Time 3. Hope was not significantly associated with changes in self-esteem at either follow-up assessment. CONCLUSION: People with high levels of hope appear to be better able to adjust to the challenges faced once they leave the rehabilitation center. Psycho-educational interventions that promote agency and pathway thinking may lead to better longer-term adjustment. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".