MétaCan
Menu
Back to cohort
Record W4376609784 · doi:10.1186/s12889-023-15586-y

Using the COM-B model and Behaviour Change Wheel to develop a theory and evidence-based intervention for women with gestational diabetes (IINDIAGO)

2023· article· en· W4376609784 on OpenAlexafffund
Katherine Murphy, J. Edward Berk, Lorrein Shamiso Muhwava, Sharmilah Booley, Janetta Harbron, Lisa J. Ware, Shane A. Norris, Christina Zarowsky, Estelle V. Lambert, Naomi Levitt

Bibliographic record

VenueBMC Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersDSI-NRF Centre of Excellence in Human Development, University of the Witwatersrand, JohannesburgInternational Development Research Centre
KeywordsMedicineGestational diabetesOverweightIntervention (counseling)Public healthPsychosocialPsychological interventionIntervention mappingFamily medicineNursingHealth promotionPregnancyObesityPsychiatryGestation

Abstract

fetched live from OpenAlex

BACKGROUND: In South Africa, the prevalence of gestational diabetes (GDM) is growing, concomitant with the dramatically increasing prevalence of overweight/obesity among women. There is an urgent need to develop tailored interventions to support women with GDM to mitigate pregnancy risks and to prevent progression to type 2 diabetes post-partum. The IINDIAGO study aims to develop and evaluate an intervention for disadvantaged GDM women attending three large, public-sector hospitals for antenatal care in Cape Town and Soweto, SA. This paper offers a detailed description of the development of a theory-based behaviour change intervention, prior to its preliminary testing for feasibility and efficacy in the health system. METHODS: The Behaviour Change Wheel (BCW) and the COM-B model of behaviour change were used to guide the development of the IINDIAGO intervention. This framework provides a systematic, step-by-step process, starting with a behavioural analysis of the problem and making a diagnosis of what needs to change, and then linking this to intervention functions and behaviour change techniques to bring about the desired result. Findings from primary formative research with women with GDM and healthcare providers were a key source of information for this process. RESULTS: Key objectives of our planned intervention were 1) to address women's evident need for information and psychosocial support by positioning peer counsellors and a diabetes nurse in the GDM antenatal clinic, and 2) to offer accessible and convenient post-partum screening and counselling for sustained behaviour change among women with GDM by integrating follow-up into the routine immunisation programme at the Well Baby clinic. The peer counsellors and the diabetes nurse were trained in patient-centred, motivational counselling methods. CONCLUSIONS: This paper offers a rich description and analysis of designing a complex intervention tailored to the challenging contexts of urban South Africa. The BCW was a valuable tool to use in designing our intervention and tailoring its content and format to our target population and local setting. It provided a robust and transparent theoretical foundation on which to develop our intervention, assisted us in making the hypothesised pathways for behaviour change explicit and enabled us to describe the intervention in standardised, precisely defined terms. Using such tools can contribute to improving rigour in the design of behavioural change interventions. TRIAL REGISTRATION: First registered on 20/04/2018, Pan African Clinical Trials Registry (PACTR): PACTR201805003336174.

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)0.016
metaresearch head score (Gemma)0.014
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: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
GPT teacher head0.411
Teacher spread0.165 · 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
GenreMethods

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

Citations37
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueBMC Public HealthSame topicGestational Diabetes Research and ManagementFrench-language works237,207