Facilitators and Barriers for Implementing an Internet Clinic for the Treatment of Pressure Injuries
Bibliographic record
Abstract
Background: Pressure injuries (PIs) represent a frequent, often preventable, secondary complication of spinal cord injury (SCI) with serious consequences to health, societal participation, and quality of life. Specialized knowledge and service delivery related to treatment and prevention are typically located within major health centers. Introduction: For persons with SCI living at home, it can be challenging to access specialized PI care. A telehealth approach could help mitigate this challenge. This multisite pilot investigation assessed the feasibility of integrating information technologies within the management of PIs. Materials and Methods: Each study site formed a specialized interdisciplinary care team that identified components of their standard clinical care pathway and examined how they could be integrated with study technologies. A monitoring system was utilized to enable patients and caregivers to exchange clinical information with the care team. Results: Clinician and patient focus groups were completed to identify facilitators and barriers for long-term implementation. Findings demonstrate that this method of service delivery is feasible but requires further development. Discussion: This model of care requires refinement to address technological, regulatory, and clinician acceptance barriers; however, increased access to these services has the potential for improving PI healing or prevention rates in comparison with those not able to access specialized services. Conclusions: This project demonstrates that PI treatment services can be delivered effectively through the internet. Future trials can investigate efficacy and cost-effectiveness of this model of care to inform sustained implementation.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".