A Scoping Review of Self-Management Interventions Following Spinal Cord Injury
Bibliographic record
Abstract
Objective: To conduct a scoping review to identify what components of self-management are embedded in self-management interventions for spinal cord injury (SCI). Methods: In accordance with the approach and stages outlined by Arksey and O'Malley (2005), a comprehensive literature search was conducted using five databases. Study characteristics were extracted from included articles, and intervention descriptions were coded using Practical Reviews in Self-Management Support (PRISMS) (Pearce et al, 2016), Barlow et al (2002), and Lorig and Holman's (2003) taxonomy. Results: A total of 112 studies were included representing 102 unique self-management programs. The majority of the programs took an individual approach (52.0%) as opposed to a group (27.4%) or mixed approach (17.6%). While most of the programs covered general information, some provided specific symptom management. Peers were the most common tutor delivering the program material. The most common Barlow components included symptom management ( n = 44; 43.1%), information about condition/treatment ( n = 34; 33.3%), and coping ( n = 33; 32.4%). The most common PRISMS components were information about condition and management ( n = 85; 83.3%), training/rehearsal for psychological strategies ( n = 52; 51.0%), and lifestyle advice and support ( n = 52; 51.0%). The most common Lorig components were taking action ( n = 62; 60.8%), resource utilization ( n = 57; 55.9%), and self-tailoring ( n = 55; 53.9%). Conclusion: Applying self-management concepts to complex conditions such as SCI is only in the earliest stages of development. Despite having studied the topic from a broad perspective, this review reflects an ongoing program of research that links to an initiative to continue refining and testing self-management interventions in SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".