Exploring the Quality of Life of People with Incomplete Spinal Cord Injury Who Can Ambulate
Bibliographic record
Abstract
(1) Purpose: To examine associations between subjective quality of life and other socio-demographic variables and to explore differences in experiences of people with different levels of quality of life (low, moderate, high). (2) Materials and methods: Semi-structured interviews and standardized measures of mobility, function, health-related quality-of-life, and quality-of-life were used to collect the data for this mixed-method study. (3) Results: Twenty-four participants were interviewed with an average age of 55 years and 54% were male. High quality of life, according to quantitative analysis, was strongly associated with being male, attending rehabilitation, and being married. The qualitative findings supported the quantitative findings and also revealed that people with a low quality of life felt the neighborhood-built environment was not supportive of people with incomplete spinal cord injury who can walk. Participants who reported a low/moderate quality of life reported feeling devalued by able-bodied people and that their mobility was getting worse over time. (4) Conclusion: Findings suggest that those with incomplete spinal cord injuries who can walk could benefit from improved quality of life by modifying their social support and neighborhood’s built environment. For instance, sensitivity training for the general population could help to reduce negative attitudes and misperceptions about invisible impairments and promote inclusion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".