Reaching out to diabetic soles: Outreach foot care pilot project
Bibliographic record
Abstract
OBJECTIVES: To assess the effectiveness of outreach foot care services as a tool for engagement with isolated vulnerable seniors. To improve foot health of diabetic seniors, thus avoiding expensive and potentially life-threatening diabetic complications. METHODS: Four validated tools are used to gather data: InLow 60-second Diabetic Foot Screen©, Short Diabetes Knowledge Instrument for Older and Minority Adults, Brief Healthcare Questionnaire (Patient Health Questionnaire-9), and the Health-Related Quality of Life Questionnaire. RESULTS: Five monthly visits to 20 participants resulted in multiple co-morbidities being identified, improvements in foot status and diabetic knowledge realized, and determinants of health addressed. Seniors needed support and resources to engage in diabetes self-management. CONCLUSION: The importance of regular foot care as a key element of any self-management plan for diabetes cannot be understated, nor can increasing social services spending to include coverage for foot care thereby avoiding expensive healthcare. Using foot care as a tool for engagement conferred access to vulnerable seniors who ultimately benefited from healthcare and social interactions with a provider.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".