Experiences of a Novel Structured Foot Examination Form for Patients With Diabetes From the Perspective of Health Care Professionals: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Diabetes is a growing threat to public health, and secondary diseases like foot complications are common. Foot ulcers affect the individual's quality of life and are a great cost to society. Regular foot examinations prevent foot ulcers and are a recommended approach both in Sweden and worldwide. Despite existing guidelines, there are differences in the execution of the foot examination, which results in care inequality. A structured foot examination form based on current guidelines was developed in this study as the first step toward digitalized support in the daily routine, and was validated by diabetes health care professionals. OBJECTIVE: The study aimed to validate a structured foot examination form by assessing health care professionals' experiences of working with it "foot side" when examining patients with diabetes. METHODS: Semistructured interviews were held in a focus group and individually with 8 informants from different diabetes professions, who were interviewed regarding their experiences of working with the form in clinical practice. The users' data were analyzed inductively using qualitative content analysis. The study is part of a larger project entitled "Optimised care of persons with diabetes and foot complications," with Västra Götaland Region as the responsible health care authority, where the results will be further developed. RESULTS: Experiences of working with the form were that it simplified the foot examination by giving it an overview and a clear structure. Using the form made differences in work routines between individuals apparent. It was believed that implementing the form routinely would contribute to a more uniform execution. When patients had foot ulcers, the risk categories (established in guidelines) were perceived as contradictory. For example, there was uncertainty about the definition of chronic ulcers and callosities. The expectations were that the future digital format would simplify documentation and elucidate the foot examination, as well as contribute to the accessibility of updated and relevant data for all individuals concerned. CONCLUSIONS: The foot examination form works well as a support tool during preventive foot examination, creates a basis for decision-making, and could contribute to a uniform and safer foot examination with more care equality in agreement with current guidelines. TRIAL REGISTRATION: ClinicalTrials.gov NCT05692778; https://clinicaltrials.gov/ct2/show/NCT05692778.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".