Inefficient procurement processes undermine access to medicines in the Western Cape Province of South Africa
Bibliographic record
Abstract
BACKGROUND: South Africa (SA) has experienced several stock-outs of life-saving medicines for the treatment of major chronic infectious and non-communicable diseases in the public sector. OBJECTIVE: To identify the causes of stock-outs and to illustrate how they undermine access to medicines (ATM) in the Western Cape Province, SA. METHODS: This qualitative study was conducted with a sample of over 70 key informants (frontline health workers, sub-structure and provincial health service managers). We employed the critical incident technique to identify significant occurrences in our context, the consequences of which impacted on access to medicines during a defined period. Stock-outs were identified as one such incident, and we explored when, where and why they occurred, in order to inform policy and practice. RESULTS: Medicines procurement is a centralised function in SA. Health service managers unanimously agreed that stock-outs resulted from the following inefficiencies at the central level: (i) delays in awarding of pharmaceutical tenders; (ii) absence of contracts for certain medicines appearing on provincial code lists; and (iii) suppliers' inability to satisfy contractual agreements. The recurrence of stock-outs had implications at multiple levels: (i) health facility operations; (ii) the Chronic Dispensing Unit (CDU), which prepacks medicines for over 300 000 public sector patients; and (iii) community-based medicines distribution systems, which deliver the CDU's prepacked medicines to non-health facilities nearer to patient homes. For instance, stock-outs resulted in omission of certain medicines from CDU parcels that were delivered to health facilities. This increased workload and caused frustration for frontline health workers who were expected to dispense omitted medicines manually. According to frontline health workers, this translated into longer waiting times for patients and associated dissatisfaction. In some instances, patients were asked to return for undispensed medication at a later date, which could potentially affect adherence to treatment and therapeutic outcomes. Stock-outs therefore undermined the intended benefits of ATM strategies. CONCLUSION: Addressing the procurement challenges, most notably timeous tender awards and supplier performance management, is critical for successful implementation of ATM strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".