‘The solution is we need to have a centre’: a study on diabetes in Liberia
Bibliographic record
Abstract
In Liberia, one of the poorest nations in sub-Saharan Africa, the burden of diabetes is a growing concern. The high mortality and morbidity associated with diabetes have significant implications for individuals, families and society at large. The aim of this critical hermeneutic study was to explore what it is like to live with diabetes in Liberia. We recruited 10 participants from Monrovia, Liberia to partake in this study. Photovoice, a well-established participatory data collection approach was used to gather images and stories that represented participants' everyday experiences of living with diabetes. Three major themes were uncovered, highlighting the strengths, challenges and solutions related to living with diabetes in Liberia: strengths-engagement in diabetes self-management practices, focused on participants' commitment to engage in diabetes self-management practices despite the socioeconomic challenges they experienced; challenges-lack of social and economic support, focused on limited access to food, diabetes medications and supplies and diabetes education; and solutions-centre for diabetes education, care and support, focused on participants' recommendations for a community-based diabetes centre, a single point of access for meeting the needs of people with diabetes. A strong commitment to prioritize diabetes on Liberia's national health agenda and increased resources for diabetes care is needed to address the challenges experienced by people living with this chronic disease in Liberia.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".