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Record W2904541546 · doi:10.1177/2050312118820030

Reaching out to diabetic soles: Outreach foot care pilot project

2018· article· en· W2904541546 on OpenAlexafffund
Tracey Rickards, Tammy Cornish

Bibliographic record

VenueSAGE Open Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersCanadian Institutes of Health ResearchMental Health CommissionFondation de la recherche en santé du Nouveau-Brunswick
KeywordsMedicineOutreachDiabetic footFoot (prosody)Health careQuality of life (healthcare)Diabetes managementDiabetes mellitusFamily medicineGerontologyNursingType 2 diabetes

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.383
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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