Self-management of pain and depression in adults with spinal cord injury: A scoping review
Bibliographic record
Abstract
Context: Pain and depression are two prevalent secondary complications associated with spinal cord injury (SCI) that negatively impact health and well-being. Self-management strategies are growing in popularity for helping people with SCI to cope with their pain and depression. However, there is still a lack of research on which approaches are best suited for this population.Objective: The aim of this scoping review was to determine what is known about the self-management of pain and depression through the use of pharmacological and non-pharmacological therapies in adults with SCI.Methods: Seven electronic databases were searched for articles published between January 1, 1990 and June 13, 2017. Grey literature was searched and additional articles were identified by manually searching the reference lists of included articles.Results: Overall, forty-two articles met the inclusion criteria; with the majority reporting on the self-management of pain, rather than on depression or on both complications. Non-pharmacological interventions were more likely to include self-management strategies than pharmacological interventions. A limited number of studies included all of the core self-management tasks and skills.Conclusions: There are significant knowledge gaps on effective self-management interventions for pain and depression post-SCI. There is a need to develop interventions that are multi-faceted, which include both pharmacological and non-pharmacological therapies to address multimorbidity.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".