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Record W2887380804 · doi:10.1136/bmjebm-2018-111024.67

67 Using deliberative priority-setting to improve gestational diabetes education

2018· article· en· W2887380804 on OpenAlexaffabout
Roseanne O. Yeung, Jamie Boisvenue, Padma Kaul, Edmond A. Ryan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGestational diabetesFeelingPregnancyMedicineMultidisciplinary approachNursingSession (web analytics)Patient educationHealth careFamily medicineMedical educationPsychologyComputer scienceGestationSocial psychology

Abstract

fetched live from OpenAlex

Objectives Gestational diabetes mellitus (GDM) education is an important component of GDM management. While pregnancy is thought to be a generally positive experience for women, those with GDM are asked to manage their pregnancy under constant self-discipline to avoid pregnancy complications from hyperglycemia. For many with GDM, this experience is overwhelming. The guidance provided by a multidisciplinary health care team in delivering gestational diabetes education and acknowledging the emotional impact of a diagnosis has shown to improve self-care behaviours and subsequent birth outcomes. This study aims to explore GDM education and care experiences amongst women diagnosed with GDM attending publicly provided education classes at diabetes clinics in Edmonton, Alberta, Canada. Method Deliberative priority-setting was the methodology used as described by the Canadian Institute of Health Research (CIHR) to establish a dialogue throughout six working sessions with 5 women with GDM and 7 diabetes health care providers. Iterative working sessions assessed opinions on educational material provided in classes, feelings and emotions surrounding GDM, and how the healthcare system can improve to better meet their needs. Each session was transcribed and a priority-setting and website assessment surveys were conducted. Results We identified twelve priorities from the priority-setting survey that women wanted to be addressed beyond the existing GDM classes. These include future impacts of GDM on mother and child, blood glucose number interpretation; insulin administration instruction; GDM pathophysiology; how to manage GDM when basic necessities and support are unavailable; language and culture-specific materials; mental health and emotional management and ensuring consistent communication and messaging from health care providers. The working sessions also revealed that the www.diabetes-pregnancy.ca website is a commonly used resource across clinics in this region, however, not all clinicians provided or recommended women visit this site. Through the website assessment survey, women identified inconsistencies within content compared to what was delivered in class and were more interested in having access to site content that focus on patient narrative through text and videos that is relatable to with practical advice that can be applied to daily self-management. Conclusions A priority-setting partnership between women with GDM, healthcare providers, and researchers allowed for honest dialogue on issues relevant to health care providers and women living with GDM. This identified issues that were not adequately addressed in the existing standard GDM education. Women with GDM and health care providers identified the need for consistent and readily accessible information and determined a priority list of items that they would find most helpful. The use of an online resource that women can access before and after attending a GDM education class may help solidify learning and improve self-care behaviours.

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)0.050
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.005
Scholarly communication0.0050.004
Open science0.0020.012
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.015
GPT teacher head0.320
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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