Key stakeholders’ views on the causes of medicine stock-outs in Mauritania: A qualitative study
Bibliographic record
Abstract
The number of medicine stock-outs is increasing globally. In Mauritania, they are recurring, although, to our knowledge, no study has yet been conducted to determine the causes. Therefore, this qualitative study aims to explore the views of key stakeholders in the pharmaceutical sector to identify the main local or national causes of stock-outs. It will thus provide a common understanding and guide policy-makers towards corrective action. The study was carried out in five health districts and at the regional and central levels. The samples were purposive. Two focus groups and twenty semi-structured individual interviews were held with 38 participants, including health professionals, managers from the Central Purchasing Office for Essential Medicines and Consumables, the Pharmacy and Laboratory Department and the Ministry of Health. All interviews were recorded and transcribed. A thematic content analysis was carried out. Our findings indicate the national causes of medicine stock-outs at three healthcare system levels (operational, regional, and central). They were grouped into five categories: insufficient human resource capacity (number of staff, training, retention), communication and coordination problems between stakeholders, logistical constraints (transport, storage), financial constraints, inadequate forecasting of needs, and complex procurement procedures. These causes of medicine stock-outs are interconnected, and many could be addressed locally through solutions initiated and led by the Mauritanian authorities. To address medicine stock-outs sustainably, we suggest and discuss some possible actions, including reforms to improve Central Purchasing Office for Essential Medicines and Consumables's governance and accountability and, more broadly, to strengthen the various pillars of the local health and pharmaceutical system.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".