Developing a Model of Care for Healing Pressure Ulcers With Electrical Stimulation Therapy for Persons With Spinal Cord Injury
Bibliographic record
Abstract
Background: Electrical stimulation therapy (EST) has been shown to be an effective therapy for managing pressure ulcers in individuals with spinal cord injury (SCI). However, there is a lack of uptake of this therapy, and it is often not considered as a first-line treatment, particularly in the community. Objective: To develop a pressure ulcer model of care that is adapted to the local context by understanding the perceived barriers and facilitators to implementing EST, and to describe key initial phases of the implementation process. Method: Guided by the Knowledge-to-Action (KTA) and National Implementation Research Network (NIRN) frameworks, a community-based participatory research (CBPR) approach was used to complete key initial implementation processes including (a) defining the practice, (b) identifying the barriers and facilitators to EST implementation and organizing them into implementation drivers, and (c) developing a model of care that is adapted to the local environment. Results: A model of care for healing pressure ulcers with EST was developed for the local environment while taking into account key implementation barriers including lack of interdisciplinary collaboration and communication amongst providers between and across settings, inadequate training and education, and lack of resources, such as funding, time, and staff. Conclusions: Using established implementation science frameworks with structured planning and engaging local stakeholders are important exploratory steps to achieve a successful sustainable best practice implementation project.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".