An investigation of factors affecting patients waiting time in primary health care centers: An assessment study in Dubai
Bibliographic record
Abstract
This study tends to investigate and assess the average waiting time (WT) in Dubai primary healthcare services centers. Healthcare centers will face critical problems if WT is not solved properly. Accordingly, this study tries to dig a deep insight on such problem and provides proper suggestions to reduce WT. An Electronic Medical Record audit is used to count the patients’ WT during a four-week period in health care service centers employing a universal sampling approach. All patients who visit the health medical centers during such period are considered for the study purpose except those need emergency services. A self-administered questionnaire is used to collect the needed records about WT longevity causes from direct em-ployees who use to interact patients in a continuous basis. The questionnaires are distributed in 12 healthcare centers throughout Emirate of Dubai in UAE. A total of 76,780 electronic medical records are audited for patients and 938 responses are analyzed for the employee survey. The study finds that about 45.2% of the patients were registered within less than 7 minutes of their visit and the mean WT was 11.7 minutes of entrance. While more than two third of them (75.3%) waited less than 30 minutes and the average consultation WT was 34.2 minutes. 65.9% of patients waited less than 28 days to get their appointment and the average appointment WT was 35 days. The data collected from employees denoted that the main causes of patients’ WT were high workload level, insufficient work procedure, employees-supervisor interaction problems and adequate facilities availability. There is a need for healthcare leaders and managers in charges in this sector to reduce patients’ complaints while waiting and to solve the WT problem in a planned manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".