Development of Telenursing Guidelines to Improve the Quality of Services in Diabetic Wound Care in a Hospital in Thailand: Case Study
Bibliographic record
Abstract
Background: The majority of patients with diabetic wounds living in Mae Chan District, Chiang Rai Province face challenges such as a shortage of nurses, limited access to health care, and insufficient resources. Strategies such as specialist networks, patient monitoring, and online care platforms are crucial to improving diabetic wound management in the community. Objective: This study aims to develop telenursing guidelines for caring for patients with diabetic wounds and foot ulcers, and to investigate the effects of telenursing on wound healing among patients. Methods: Participatory action research was conducted in three cycles: (1) assessing the current situation and feasibility of telenursing; (2) evaluating telenursing guidelines for wound healing; and (3) examining the effects of telenursing on wound healing, amputation rates, and patient satisfaction. Results: The mean diabetic wound severity scores decreased after receiving telenursing care at weeks 2, 4, 6, and 8 (P<.001). No patients were found to have foot or leg amputations. The patients in the group who received telenursing care showed that their wounds healed in an average of 8.6 (SD 4.3) weeks. The satisfaction score for telenursing care was 4.7 out of 5 (SD 0.2). Conclusions: Telenursing guidelines were developed to enhance access to wound care, reduce amputation rates, and promote wound healing, resulting in a significant reduction in wound severity and the absence of amputations. The study further demonstrated that telenursing not only expedited healing times but also reduced health care costs and improved patient satisfaction.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".