Sourcing Strategy and Supply Chain Risk Management in the Healthcare Sector: A Case Study of Malawi’s Public Healthcare Delivery Supply Chain
Bibliographic record
Abstract
The study primarily investigated the role of single sourcing strategy in either exacerbating or mitigating persistent supply failure in Malawi’s public healthcare delivery supply chain. It also investigated the role of the single supplier in recurrent drug stockouts in the country’s public hospitals. The study was conducted at five public healthcare delivery centres in southern Malawi. Data was collected using two sets of self administered questionnaires to, hospital managers and procurement staff at participating hospitals. Follow up interview with randomly selected managers were also scheduled to complement the data from questionnaires. The study findings confirmed that single sourcing exacerbates the risk of supply failure, evidenced by persistent stockouts of drugs in hospitals that consequently put at risk lives of many patients who rely on the public healthcare system. Such stockouts had multiple effects on the public healthcare delivery supply chains ranging from death of patients, deterioration of medical conditions of patients, hospital overcrowding, to transfer of patients to other hospitals (mostly private). Failure by the government instituted single supplier namely the Central Medical Stores, to maintain an adequate inventory of drugs both in quantity and variety, was found to be the most important cause of stockouts. From the study, it has been shown that the single sourcing strategy exacerbated the risk of supply failure, and is therefore generally inappropriate for healthcare delivery systems. It is hoped that these findings will help policy makers in making the right procurement strategies and policies in order to enhance healthcare delivery.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".