Self-management interventions for skin care in people with a spinal cord injury: part 2—a systematic review of use of theory and quality of intervention reporting
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVES: To examine use of theory and quality of reporting in skin care self-management interventions for people with SCI. SETTING: International. METHODS: The Theory Coding Scheme (TCS) and the Template for Intervention Description and Replication (TIDieR) checklist were applied by two independent researchers to 17 interventions identified in a systematic review of self-management interventions for skin care in people with SCI. RESULTS: Six (35%) of the 17 interventions reviewed were reported to have a theoretical basis. Theories used included three of the most commonly featured in health behavior research (the Health Belief Model, Social Cognitive Theory, and the Transtheoretical Model). In these six interventions, theory was used to design content but not to select participants or tailor strategies. None of the interventions were used to test theories in the SCI population, or to propose theoretical refinements. Reporting quality was found to vary by TIDieR item, with 6-100% of interventions including recommended information. Information on two intervention fidelity items was missing in 53 and 82% of descriptions. CONCLUSIONS: Use of theory and reporting quality in SCI self-management research remains suboptimal, potentially slowing down advancements in this area of research. Rehabilitation researchers should direct their efforts toward improving these practices to help build a science of SCI self-management that is cumulative and reproducible by clinicians, scientists, and policy makers. SPONSORSHIP: This work was funded through a postdoctoral fellowship awarded to the first author by the Rick Hansen Institute.
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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 | 0.094 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".