ArticleBJGP open2024
Personalised care and support planning in Singapore: qualitative interviews with people living with diabetes.
Article in BJGP open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Markov Decision Process-Based Personalized Follow-Up Planning for Type 2 Diabetes: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Article
- Understanding the Experiences of Women with Gestational Diabetes in Singapore: A Qualitative Study.International journal of women's health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPersonalised care and support planning (CSP) is a person-centred approach for the care of people living with long-term conditions. Patient Activation through Community Empowerment/Engagement for Diabetes Management (PACE-D) adapts the Year of Care Partnerships (YOCP) approach to CSP in the UK for people living with diabetes at Singapore polyclinics. Polyclinics are multi-storey primary care hubs that provide affordable, multidisciplinary, comprehensive, and high-throughput public health care for the multi-ethnic, multilingual Singapore population.
aimTo explore the experience of PACE-D-enrolled people living with diabetes with personalised CSP at Singapore polyclinics. DESIGN &
settingQualitative interviews of people living with diabetes who experienced personalised CSP at National University Polyclinics (NUP) in Singapore between July 2020 and November 2021.
methodPACE-D-enrolled people living with diabetes who experienced personalised CSP were purposively sampled. In-depth semi-structured interviews were recorded, transcribed, and analysed using Braun and Clarke's reflexive thematic analysis.
resultsFifty-two patients participated in the study. Four main themes were identified. Theme 1 was the importance of the care-planning letter. Patients reported that the CPL prompted reflection and patient preparation for CSP conversations. Theme 2 was the role of the programme coordinator. PACE-D programme coordinators amplified self-management by playing advocate and confidant beyond administrative duties. Theme 3 was the value of the personalised CSP conversation. CSP providers were perceived as partners in care, with more time to listen compared with usual consultations. Patient engagement was affected by language confidence. Theme 4 was agency in self-management. With adequate time and support, patients increased in confidence and agency both in CSP engagement and diabetes self-management.
conclusionWhile language confidence may affect patient engagement, personalised CSP shows promise for strengthening patient engagement and self-management among people living with diabetes at Singapore polyclinics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.