Complex lived experiences and hidden disability after spinal cord injury: a latent profile analysis of the Australian arm of the International Spinal Cord Injury (Aus-InSCI) Community Survey
Bibliographic record
Abstract
Purpose To identify and examine subgroups of people with spinal cord injury (SCI) with different patterns of lived experience, and examine hidden impairments and disability among functionally independent and ambulant people.Materials and methods Latent profile analysis of population-based data from the Australian arm of the International Spinal Cord Injury (InSCI) Community survey (n = 1579).Results Latent subgroups reflected levels of functional independence and extent of problems with health, activity/participation, environmental barriers, and self-efficacy. Quality of life (QoL), psychological profiles, and activity/participation were often as good or better in participants who reported lower (vs. higher) functional independence alongside comparable burden of health problems and environmental barriers. QoL, mental health, and vitality reflected self-efficacy and problem burdens more closely than functional independence. Ambulant participants reported a substantial burden of underlying, potentially hidden impairments, with QoL and mental health similar to wheelchair users.Conclusion Hidden disability among more independent and/or ambulant people with SCI can affect well-being substantially. Early and ongoing access to support, rehabilitation, and SCI specialist services is important irrespective of cause, type, severity of injury, and level of functional independence. Improved access to SCI expertise and equity of care would help to improve early recognition and management of hidden disability.Trial registration Not applicable.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".