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Record W1990316762 · doi:10.1080/09638280400020631

Disability management practices in Ontario workplaces: Employees' perceptions

2005· article· en· W1990316762 on OpenAlexaffabout
Muriel G. Westmorland, Renee Williams, Ben Amick, Harry S. Shannon, Farah Rasheed

Bibliographic record

VenueDisability and Rehabilitation · 2005
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute for Work & HealthMcMaster University
FundersU.S. Public Health Service
KeywordsFocus groupCronbach's alphaPsychologyOccupational safety and healthApplied psychologyRetrainingPerceptionNursingMedical educationMedicineBusinessMarketingPsychometricsClinical psychology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to obtain employees' perceptions about disability management (DM) at their workplaces. METHODS: Data were obtained from focus group interviews and individual telephone interviews with 58 employees who had sustained a work-related injury or disability in Ontario, Canada. Participants also completed a 22-item Organizational Policies and Practices (OPP) Questionnaire that asked questions about workplace DM practices. RESULTS: Respondents emphasized the need for job accommodation, the importance of open and clear communication and the necessity of job retraining. The provision of ergonomic modifications to their worksites and the development of meaningful and specific DM policies and procedures were seen as key to a comprehensive workplace DM program. Education about health and safety also was identified as an important component of creating a supportive workplace environment. The OPP questionnaire showed good internal consistency (Cronbach's alpha=0.95) and discriminant validity. CONCLUSION: This study demonstrates the importance of workplaces communicating with their employees and respecting their opinions when establishing and carrying out DM policies and practices. The OPP Questionnaire is useful in determining how DM is managed in the workplace.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.315
Teacher spread0.296 · 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 teacher head, 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

Citations47
Published2005
Admission routes2
Has abstractyes

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