The Relationship Between Supplier Consistency Appraisal and Procurement Implementation in Public Hospitals in Mandera County, Kenya
Bibliographic record
Abstract
Aim: Public hospitals in Mandera County, Kenya, continue to experience procurement execution challenges such as delays, noncompliance with procurement protocols, and inconsistent supplier performance. The study aimed to examine the relationship between supplier consistency appraisal and procurement implementation in public hospitals in Mandera County, Kenya. Methods: An explanatory research design was adopted. The target population was 303 respondents, comprising the procurement officers, accountants, and administration managers in the 101 public hospitals in Mandera. A sample size of 171 respondents was recruited for the study. Stratified sampling was used to select the respondents. Primary data were collected through structured questionnaires designed on a Likert scale. Data analysis used both descriptive and inferential statistics. The inferential statistics were correlation and regression analysis. Results: The study found that supplier appraisal practices significantly influence procurement implementation in public hospitals in Mandera County, Kenya (β=0.866, p=0.000). Conclusion: The study concludes that strengthening supplier consistency appraisal enhances transparency, accountability, and the overall efficiency of procurement systems in public hospitals. Recommendations: The study recommends that public hospitals in Mandera County enhance supplier consistency appraisal through digital systems that monitor delivery timelines, quality, and compliance. Cross-functional review teams and regular audits should be implemented to strengthen reliability. The Ministry of Health should establish a national supplier appraisal framework with capacity and risk metrics, mandate annual performance audits, and support procurement digitization through funding and training to promote transparency, accountability, and efficiency in public healthcare procurement.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".