Telemedicine in diabetes foot care delivery: health care professionals’ experience
Bibliographic record
Abstract
BACKGROUND: Introducing new technology in health care is inevitably a challenge. More knowledge is needed to better plan future telemedicine interventions. Our aim was therefore to explore health care professionals' experience in the initial phase of introducing telemedicine technology in caring for people with diabetic foot ulcers. METHODS: Our methodological strategy was Interpretive Description. Data were collected between 2014 and 2015 using focus groups (n = 10). Participants from home-based care, primary care and outpatient hospital clinics were recruited from the intervention arm of an ongoing cluster randomized controlled trial (RCT) (Clinicaltrials.gov: NCT01710774). Most were nurses (n = 29), but the sample also included one nurse assistant, podiatrists (n = 2) and physicians (n = 2). RESULTS: The participants reported experiencing meaningful changes to their practice arising from telemedicine, especially associated with increased wound assessment knowledge and skills and improved documentation quality. They also experienced more streamlined communication between primary health care and specialist health care. Despite obstacles associated with finding the documentation process time consuming, the participants' attitudes to telemedicine were overwhelmingly positive and their general enthusiasm for the innovation was high. CONCLUSIONS: Our findings indicate that using a telemedicine intervention enabled the participating health care professionals to approach their patients with diabetic foot ulcer with more knowledge, better wound assessment skills and heightened confidence. Furthermore, it streamlined the communication between health care levels and helped seeing the patients in a more holistic way.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".