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Record W4285103780 · doi:10.2196/38477

Capturing Diabetes-Related Distress and Burden From the Perspective of Patients With Type 1 or Type 2 Diabetes: Protocol for an Explorative Mixed Methods Study

2022· article· en· W4285103780 on OpenAlexvenueno aff
Sandra Zara, Johannes Kruse, Anna Maria Leveling, Jana Schmitz, Isabelle Hempler, Hanna Kampling

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsDistressMedicineDiabetes mellitusPsychosocialType 2 diabetesDiabetes managementAnxietyType 1 diabetesQuality of life (healthcare)Research designDiseaseIntensive care medicineClinical psychologyPsychiatryNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetes is one of the most common diseases worldwide and is associated with increased morbidity, mortality, and reduced quality of life. Many patients experience high diabetes-related distress as well as depression and anxiety symptoms, which are associated with poor diabetes self-management. As disease management is a central component in diabetes treatment, poor management enhances the occurrence of micro- and macrovascular complications. This emphasizes the relevance of reducing diabetes-related distress and providing adequate treatment options addressing the individual psychosocial burden of patients with diabetes. Since patients' perspectives diverge significantly from those of practitioners in terms of relevant treatment aspects, the patient perspective on, for example, barriers to and facilitators of diabetes treatment is crucial for adequate and effective treatment as well as improvements to self-management and therefore, needs to be further explored. OBJECTIVE: This study aims to examine diabetes-related distress, the course of distress throughout diabetes management, as well as barriers and facilitating factors in dealing with diabetes from the individual perspective of patients with type 1 and type 2 diabetes. METHODS: The study employs a mixed methods design combining qualitative and quantitative data. Semistructured interviews (N=40) will be conducted with patients with type 1 diabetes (n=20) and patients with type 2 diabetes (n=20). The primary outcomes comprise (1) diabetes-related distress, (2) the severity of distress, (3) the course of distress throughout diabetes management, (4) barriers, and (5) facilitating factors. Questionnaires will provide data on the following secondary outcomes: diabetes-related emotional distress (the Problem Areas in Diabetes scale), symptoms of depression and anxiety (Patient Health Questionnaire, German version), personality functioning (Operationalized Psychodynamic Diagnosis-Structure Questionnaire), mentalizing capacities (Mentalization Questionnaire), epistemic trust (Epistemic Trust, Mistrust and Credulity Questionnaire) and experiences of child maltreatment (Childhood Trauma Questionnaire), and the overall health status of the patient (routine medical data). RESULTS: As of April 2022, the conceptualization phase of the study was finalized. Ethics approval was received in January 2022 from the local ethics committee of the Justus Liebig University Giessen - Faculty of Medicine (AZ 161/21). CONCLUSIONS: This study will provide insights into the individual perspective of patients with type 1 and type 2 diabetes regarding their experiences with diabetes management and what they perceive to be relevant, obstructive, or beneficial. The insights gained could help further tailor diabetes treatment to the individual needs of patients with diabetes and therefore optimize diabetes self-management. TRIAL REGISTRATION: German Clinical Trial Register DRKS00024999; https://tinyurl.com/2wb4xdh8. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/38477.

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 imitation

Not 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.

metaresearch head score (Codex)0.087
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.087
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.050
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0050.006
Science and technology studies0.0070.003
Scholarly communication0.0040.003
Open science0.0050.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0390.007

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 head0.253
GPT teacher head0.556
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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

Citations7
Published2022
Admission routes1
Has abstractyes

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