Type 1 diabetes care delivery in Yaoundé, Cameroon: Social and political representations
Bibliographic record
Abstract
BACKGROUND: Increasing chronic diseases challenges the health systems of low- and middle-income countries, including Cameroon. Type 1 diabetes (T1D), among the most common chronic diseases in children, poses particular care delivery challenges. AIM: We examined social representations of patients' roles and implementation of T1D care among political decision-makers, healthcare providers and patients within families. SETTING: The study was conducted in Yaoundé, Cameroon. METHODS: Eighty-two individuals were included in the study. The authors conducted semi-structured interviews with policy makers (n = 5), healthcare professionals (n = 7) and patients 'parents (n = 20). Questionnaires were administered to paediatric patients with T1D (n = 50). The authors also observed care delivery at a referral hospital and at a T1D-focused non-governmental organisation over 15 days. Data were analysed using thematic content analysis and descriptive statistics. RESULTS: Cameroonian health policy portrays patients with T1D as passive recipients of care. While many practitioners recognised the complex social and economic determinants of adherence to T1D care, in practice interactions focused on specific biomedical issues and offered brief guidance. Cultural barriers and policy implementation challenges prevent patients and their families from being fully active participants in care. Parents and children prefer an ongoing relationship with a single clinician and interactions with other patients and families. CONCLUSION: Patients and families mobilise experience and lay knowledge to complement biomedical knowledge, but top-down policy and clinical practice limit their active engagement in T1D care.Contribution: Children with T1D and their families, policy makers, healthcare professionals, and civil society have new opportunities to contribute to person-centred care, as advocated by the Sustainable Development Goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".