Exploring Chiropractic Healthcare in Hong Kong: Sick Leave Certification Dilemma
Bibliographic record
Abstract
Objective This study aims to investigate the characteristics of chiropractic patients in Hong Kong, their experiences with chiropractic care, and their perspectives on chiropractors' authority over sick leave certificates. Method A cross-sectional survey was conducted among individuals receiving chiropractic treatment in Hong Kong. Data were collected through an online survey from May 11 to August 8, 2023, and descriptive analysis was employed to examine patient demographics, treatment effectiveness, and views on chiropractic sick leave authorization. A total of 522 valid responses were received. Result Among respondents, back pain was the primary reason for seeking chiropractic care, with many experiencing rapid relief and high satisfaction. However, many patients initially consulted other healthcare professionals, indicating potential integration challenges. Lengthy orthopedic wait times in Hong Kong highlight the need for chiropractic care. Concerns arose over chiropractors' inability to issue sick leave certificates, impacting patient convenience, treatment effectiveness, finances, and emotional well-being. Allowing chiropractors to authorize sick leave, with proper regulation, could address these issues. Conclusion In conclusion, this study underscores chiropractic care's potential in Hong Kong's healthcare system and suggests that recognizing chiropractors' role in sick leave authorization can enhance comprehensive patient care.
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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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".