Addressing the waiting time for elective surgeries in Hong Kong's public healthcare : a review of best practices from other developed countries
Bibliographic record
Abstract
In Hong Kong, access to elective surgeries in public hospitals is often associated with lengthy waiting times. Facing resource constraints and increasing demands from a rapidly ageing population, the Hospital Authority (HA) is constantly confronted by the healthcare rationing dilemma. To date, publicized data on elective surgery waiting times at the HA remain limited, and a standardized way of measuring waiting time is currently lacking. Recognizing that the definition of waiting time will form the basis for future policies in addressing the issue, a three-step approach will be taken in this paper. First, a comparison will be drawn for the varying definitions of waiting time worldwide. Next, a suitable definition will be proposed for Hong Kong, followed by analysis of where policy interventions are most needed for reducing waiting times. Finally, best practices for managing waiting times will be extrapolated from England, Canada, Australia, and Spain to serve as guidance for Hong Kong’s future policy direction.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".