Patients' Perceptions of Reasons Contributing to Delay in Seeking Help at the Onset of a Diabetic Foot Ulcer
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore patients' perception of reasons contributing to delay in seeking help and referral to a wound care specialist at the onset of a diabetic foot ulcer (DFU). DESIGN: Constructivist grounded theory study. SUBJECTS AND SETTING: The sample comprised 30 individuals with active DFU attending a wound care clinic in southeastern Ontario, Canada. METHODS: Participants were selected through purposive and theoretical sampling. Semistructured interviews were conducted with participants until no new properties of the patterns emerged. All interviews were transcribed, coded, and analyzed using methods informed by constructivist grounded theory. RESULTS: The reasons contributing to delay to seek help and referral to a wound care specialist were (1) limited knowledge about foot care, (2) unaware of diabetic foot problems, (3) underestimation of ulcer presentation, (4) I thought I could fix it myself, (5) inaccurate diagnosis, and (6) trial and error approach by a nonspecialized wound care provider. CONCLUSIONS: Study findings suggest that patients and primary healthcare providers need additional education regarding the management of diabetic foot disease and DFU.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".