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Record W4324365809 · doi:10.33423/jmpp.v24i1.5877

Prevalence and Predictors of Disability Management Programs

2023· article· en· W4324365809 on OpenAlexaffabout
Mike Annett

Bibliographic record

VenueJournal of Management Policy and Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsMacEwan University
Fundersnot available
KeywordsLegislatureDiversity (politics)ExcellenceLegislationBusinessWork (physics)Diversity managementSurvey data collectionPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Disability management programs are shown to speed the rate of employee returns to work, decrease recidivism, reduce the administrative costs of employee absences, and increase compliance with legislative requirements. However, formal programs are not universal - they are present in a minority of organizations. This article examines the relationship between a formal diversity management program and both organizational characteristics, and business conditions, to explain the operating contexts in which diversity management programs emerge. To answer the research questions, data from Statistica Canada’ s Workplace Employee Survey was analyzed. Findings include evidence of positive relationships for union density, operating excellence business strategy, and high involvement work practices. Negative relationships were identified for manufacturing firms, and external growth business strategy. This study provides grounding for further research on several topics, including Union-Management Collaboration, Industry Interconnectedness, and Legislation and Public Policy.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.369
Teacher spread0.336 · 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
Published2023
Admission routes2
Has abstractyes

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