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Record W3082445053 · doi:10.1186/s12913-020-05680-x

Improving systems of care during and after a pregnancy complicated by hyperglycaemia: A protocol for a complex health systems intervention

2020· article· en· W3082445053 on OpenAlexaff
Diana MacKay, Renae Kirkham, Natasha Freeman, Kirby Murtha, Paula Van Dokkum, J. Boyle, Sandra Campbell, Federica Barzi, Casey Connors, Kerin O’Dea, Jeremy Oats, Paul Zimmet, Mark Wenitong, A. Sinha, Anthony J. Hanley, Elizabeth Moore, David Peiris, Anna McLean, Bronwyn Davis, Cherie Whitbread, David McIntyre, Jacqueline Mein, Ray McDermott, Sumaria Corpus, Karla Canuto, Jonathan E. Shaw, Alex Brown, Louise Maple‐Brown, Vanya Webster, Sian Graham, Dianne Bell, Katarina Keeler, Chenoa Wapau, Martil Zachariah, Jennifer H. Barrett, Tara Dias, Kristina Vine, S. Chitturi, Sandra Eades, Christian Inglis, Karen Dempsey, Michael Lynch, Timothy Skinner, Bruce Wright

Bibliographic record

VenueBMC Health Services Research · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Toronto
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMedicineHealth informaticsNursing researchHealth administrationPublic healthProtocol (science)Intervention (counseling)PregnancyHealth services researchHealth careIntensive care medicineNursingAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Many women with hyperglycaemia in pregnancy do not receive care during and after pregnancy according to standards recommended in international guidelines. The burden of hyperglycaemia in pregnancy falls disproportionately upon Indigenous peoples worldwide, including Aboriginal and Torres Strait Islander women in Australia. The remote and regional Australian context poses additional barriers to delivering healthcare, including high staff turnover and a socially disadvantaged population with a high prevalence of diabetes. METHODS: A complex health systems intervention to improve care for women during and after a pregnancy complicated by hyperglycaemia will be implemented in remote and regional Australia (the Northern Territory and Far North Queensland). The Theoretical Domains Framework was used during formative work with stakeholders to identify intervention components: (1) increasing workforce capacity, skills and knowledge and improving health literacy of health professionals and women; (2) improving access to healthcare through culturally and clinically appropriate pathways; (3) improving information management and communication; (4) enhancing policies and guidelines; (5) embedding use of a clinical register as a quality improvement tool. The intervention will be evaluated utilising the RE-AIM framework at two timepoints: firstly, a qualitative interim evaluation involving interviews with stakeholders (health professionals, champions and project implementers); and subsequently a mixed-methods final evaluation of outcomes and processes: interviews with stakeholders; survey of health professionals; an audit of electronic health records and clinical register; and a review of operational documents. Outcome measures include changes between pre- and post-intervention in: proportion of high risk women receiving recommended glucose screening in early pregnancy; diabetes-related birth outcomes; proportion of women receiving recommended postpartum care including glucose testing; health practitioner confidence in providing care, knowledge and use of relevant guidelines and referral pathways, and perception of care coordination and communication systems; changes to health systems including referral pathways and clinical guidelines. DISCUSSION: This study will provide insights into the impact of health systems changes in improving care for women with hyperglycaemia during and after pregnancy in a challenging setting. It will also provide detailed information on process measures in the implementation of such health system changes.

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)0.100
metaresearch head score (Gemma)0.057
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.100
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1000.057
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0040.004
Science and technology studies0.0080.005
Scholarly communication0.0050.005
Open science0.0070.008
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0520.011

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 head0.083
GPT teacher head0.442
Teacher spread0.359 · 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
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

Citations16
Published2020
Admission routes1
Has abstractyes

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