MétaCan
Menu
Back to cohort
Record W4361195768 · doi:10.7759/cureus.36879

Recognizing Hong Kong Chiropractors’ Sick Leave Authority: Valuing a Conservative Approach to Workers’ Compensation

2023· review· en· W4361195768 on OpenAlexaff
Andy Fu Chieh Lin, Eric Chun‐Pu Chu, Valerie Chu, Vincent Chan, Albert C Leung, Rick P Lau, Kary K Lam, Jacky C Yeung, Kingsley Leung, Lucina Ng

Bibliographic record

VenueCureus · 2023
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsChiropracticMedicineSick leaveLegislatureScope of practiceLegislationPosition (finance)Family medicineHealth careCompensation (psychology)Government (linguistics)Alternative medicineNursingPhysical therapyLawPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Although registered under Hong Kong's legislative framework, chiropractors are not able to certify sick leave, restricting the effectiveness of their services for patients with musculoskeletal issues requiring time away from work. This paper explores the evolution of chiropractic regulation in Hong Kong, the growth of the profession, and the tardy recognition of chiropractors' power to issue sick leave certificates. The chiropractic profession and its patients have long lobbied for this authority, but the government has been slow to respond. This document presents a comprehensive analysis of the benefits and restrictions of allowing chiropractors prescriptive authority for sick leave and requests that this change in policy be considered. Developing responsible criteria for chiropractors to prescribe sick leave within their scope of practice could legitimize chiropractic's position in the population's health and interdisciplinary pain care while lowering the burden on injured workers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.185
GPT teacher head0.400
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207