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The Implementation of the Chronic Care Model with Respect to Dealing with the Biopsychosocial Aspects of the Chronic Disease of Diabetes

2011· article· en· W2314732652 on OpenAlexaff
Kathya M. Zinszer, Jennifer L. Mulhern, Ali Abdul Kareem

Bibliographic record

VenueAdvances in Skin & Wound Care · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsBiopsychosocial modelMedicineCompetence (human resources)DiseaseContinuing educationHealth carePatient educationDiabetes mellitusChronic diseaseNursingIntensive care medicineMedical educationPsychiatryPsychology

Abstract

fetched live from OpenAlex

In Brief PURPOSE: To enhance the learner's competence with information about the Chronic Care Model (CCM) with respect to dealing with the biopsychosocial aspects of diabetes. TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to: Apply information on the CCM and available assessment and evaluation tools to patient care scenarios. Correlate risk factors and outcomes for patients with combined diagnoses of depression and diabetes. Biopsychosocial illnesses, including diabetes, must be approached by clinicians who understand that not only are the biological factors, including the cause of the illness and the toll it takes on the body, important considerations, but that also psychological components experienced by the patient dealing with diabetes and social components are factors to be considered. This continuing education activity discusses the concept that understanding that integration of healthcare teams will yield higher outcomes for the patient while decreasing risk factors for comorbidities is imperative to managing chronic illnesses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.293
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2011
Admission routes1
Has abstractyes

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