Establishing a tracking system in human resources department to improve the completeness of personnel files at a district hospital in Rwanda
Bibliographic record
Abstract
Introduction: For decades, many low and mid income countries (LMIC) have invested significant effort to improve access to and quality of health care, with less attention paid to the non-clinical, administrative hospital management. Accordingly, a practical personnel filing system was designed and implemented to improve file management efficiency.Methods: Setting: The quality improvement project took place in a rural hospital in Rwanda. Design: A pre- and post-intervention study design to assess the effect of the intervention between January 2015 and February 2016. File auditing and time study were conducted. Intervention: A custom-made computer database to manage documents in a personnel file, standardized follow up process and policy were created and implemented. Measures: The pre- and post-intervention completeness of all personnel file and the average time to identify the missing items in a personnel file were measured to evaluate the effect of the project.Results: The completion rate of personnel files increased from 83% pre-intervention to 96% post-intervention. The average time to identify missing items significantly reduced from 6 minutes 30 seconds pre-intervention to 49.6 seconds (p < .001).Conclusions: This project demonstrates that quality improvement principles can help address administrative issues in a resource-challenged setting. By utilizing available resources to implement an intervention that focused on creating an easy and efficient process, the personnel file completion rate has increased considerably and the time needed to identify missing items significantly decreased. The hospital should apply the same strategic problem solving methodology to conduct other quality improvement projects.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".