Appointment structure in Malaysian healthcare system during the COVID-19 pandemic: The public perspective
Bibliographic record
Abstract
INTRODUCTION: Evidence shows physical distancing of one metre or more is important to reduce person-to-person SARS-CoV-2 transmission. This puts the Malaysian public healthcare system to a test when overcrowding has always been an issue. A new clinical appointment structure was proposed in the Malaysian public healthcare system amidst the pandemic to reduce the transmission risk. We aim to explore the general public's view on the proposed clinic appointment structure. METHODS: A cross-sectional anonymous web-based survey was conducted between 10th September 2020 and 30th November 2020. The survey was open to Malaysian aged 18 years and older via various social media platforms. The questionnaire consists of sociodemographic, experience of utilising healthcare facilities, and views on clinic appointment structure. RESULTS: A total of 1,144 complete responses were received. The mean age was 41.4 ± 12.4 years and more than half of the respondents had a preference for public healthcare. Among them, 77.1% reported to have a clinical appointment scheduled in the past. Less than a quarter experienced off-office hour appointments, mostly given by private healthcare. 70.2% answered they would arrive earlier if they were given a specific appointment slot at a public healthcare facility, as parking availability was the utmost concern. Majority hold positive views for after office hour clinical appointments, with 68.9% and 63.2% agreed for weekend and weekday evening appointment, respectively. The top reason of agreement was working commitment during office hours, while family commitment and personal resting time were the main reasons for disagreeing with off-office hour appointments. CONCLUSION: We found that majority of our respondents chose to come early instead of arriving on time which disrupts the staggered appointment system and causes over crowdedness. Our findings also show that the majority of our respondents accept off-office hour appointments. This positive response suggests that off-office hour appointments may have a high uptake amongst the public and thus be a possible solution to distribute the patient load. Therefore, this information may help policy makers to initiate future plans to resolve congestions within public health care facilities which in turn eases physical distancing during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".