Online psychosocial intervention for persons with spinal cord injury: A meta-analysis
Bibliographic record
Abstract
Background Delivery of psychosocial interventions via the Internet has the potential to overcome barriers and increase access; however, effectiveness is yet to be established among those with spinal cord injury (SCI).Methods The objective of this meta-analysis is to evaluate the efficacy of Internet-based psychosocial interventions on the symptoms of anxiety, depression, and pain amongst those with SCI. The databases Medline, PsycInfo, and EMBASE were used to locate studies published between 1990 and December 2020. A study was included if (1) the study involved the application of an online psychosocial intervention; (2) adults with SCI; and (3) reported outcomes on depression and/or anxiety. From each study, participant characteristics and study details were extracted. A standardized mean difference (SMD) ± standard error and 95% confidence interval (CI) was calculated for each outcome of interest and the results were pooled using a fixed-effects model.Results The search yielded 920 studies, of which five were included in the final meta-analysis; It was revealed that Internet-based psychosocial interventions had a small effect on reducing overall anxiety (SMD: 0.42 ± 0.09, p < 0.001) and depression (SMD: 0.41 ± 0.09, p < 0.001) symptoms at the end of the study period. Online psychosocial interventions also had a moderate effect in maintaining reduction of anxiety (SMD: 0.50 ± 0.1, p < 0.001) and depressive (SMD: 0.64 ± 0.10, p < 0.001) symptoms at 3-month follow-up.Conclusion The results of this meta-analysis provide evidence for the use of internet-based psychosocial interventions to manage anxiety and depression symptoms among those with spinal cord injuries.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".