The Applications Of Waiting Line Management On The Operations Of Public Sector Organizations: The Kogi State Health Sector Experience (2009-2012)
Bibliographic record
Abstract
The study examines the application of waiting line management on the operations of public sector organizations, a study of some selected hospitals in Kogi State. The aim of this research is to examine the application of waiting line management on the operations of public sector organizations with particular reference to the Kogi State health sector using some selected hospitals. The study used a sample of 522 patients drawn from a population of 3,090 patients, using judgmental sampling techniques. The study employed a combination of primary data in the form of questionnaire, observation and secondary data to collect data from the patients of these hospitals covering a period of (2009 – 2012). The survey research method was used in the research. The study employed chi square and ANOVA to test the necessary hypotheses. To obtain the sample size for this study, Yaro Yameru’s model of obtaining sample size was used. Other techniques used to analyze the data in this study include: queuing simulation and other queuing models. It has been observed that waiting line is one of the major problems confronting most of the public health sector, to the extent that a possible lost of life in the case of serious illness which call for emergency situation can even resulted. The study revealed among other things, that unnecessary waiting line if not properly managed has a lot of cost implications for the concerned individuals, organizations and the general public. Based on the findings and conclusions, the study recommends the outright use of the application of strategic waiting line management for both public and private hospitals in order to improve their efficiency and productivity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".