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Record W2318358798 · doi:10.1017/idm.2014.29

Studies to evaluate the outcome of DM in the public and private sector in China

2014· article· en· W2318358798 on OpenAlexaboutno aff
Karen Y. L. Lo-Hui

Bibliographic record

VenueInternational Journal of Disability Management · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsMainland ChinaPrivate sectorContext (archaeology)BusinessGovernment (linguistics)Public sectorChinaEconomic growthPromotion (chess)RehabilitationWork (physics)Political sciencePublic administrationPublic relationsMedicineGeographyEconomicsEngineeringPoliticsPhysical therapy

Abstract

fetched live from OpenAlex

Disability management (DM) is quite a fresh idea to Mainland China (Mainland). The government has thus turned to professionals from outside Mainland, i.e. Hong Kong (HK). Nevertheless, since HK is under ‘one country, two systems’ policy, it has developed an approach differing from that of Mainland. A DM pilot study was jointly conducted by the Guangdong Provincial Work Injury Rehabilitation Center (GPWIRC) and the Hong Kong Workers’ Health Centre (HKWHC) to review the developments of DM reform in China and HK. In China, the foundation of DM approach is the work injury insurance system. Under this system, GPWIRC established her services to provide work injury prevention and occupational rehabilitation as a pilot study in early 2000s. Following this pilot project, GPWIRC further develops work and social rehabilitation and work injury prevention in the context of the labor insurance system. While in HK, mainly NGOs, insurance companies and some public organizations contribute to the promotion of DM approach. On the other hand, the study also reveals similar challenges that Mainland and HK are currently facing in the development of DM, such as professional's training in local rehabilitation, the underdeveloped reimbursement system and etc.. The aforementioned pilot study shows that DM's principles are accepted at a national level and some specific public organizations in China context. There thus is a research need to study the current DM development situation in the private sector. By doing so, an ongoing study, namely “Demographic change and private sector disability management in Australia, Canada, China and Switzerland. A comparative study” launched in Nov. 2013. Through this research, questions of process and procedure of the DM system used in the company, benefits gained and drawbacks encountered by the companies, and the strengths and weaknesses in the current DM systems will be answered. Other than that, data will also be collected from the employees’ perspective on their job satisfaction, physical and mental health, employee morale and workplace attendance and etc.. The primary result is expected in 2016.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.175
GPT teacher head0.541
Teacher spread0.365 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2014
Admission routes1
Has abstractyes

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