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Record W2756282653 · doi:10.1017/idm.2017.3

Canadian Employee Perspectives on Disability Management

2017· article· en· W2756282653 on OpenAlexafffundabout
Shannon L. Wagner, Henry G. Harder, Liz Scott, Nicholas Buys, Ignatius Yu, Thomas Geisen, Christine Randall, Karen Lo, Dan Tang, Alex Fraess‐Phillips, Benedikt Hassler, Caroline Howe

Bibliographic record

VenueInternational Journal of Disability Management · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Northern British Columbia
FundersUniversity of British ColumbiaUniversity of Northern British Columbia
KeywordsJob satisfactionMental healthPsychological interventionPsychologySample (material)Work (physics)Applied psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

A Canadian sample was collected as an aspect of a large international project, with representation from Australia, Canada, China, and Switzerland. In each country, interview and survey data were collected using team-created research tools. Canadian survey data on disability management (DM) perceptions were collected from 218 employees in both public and private organisations. Our Canadian employee sample reported perceived influence of disability prevention on job satisfaction, physical health, mental health, and morale for both themselves and their coworkers. Return to work programs were seen as valuable for job satisfaction of both the employee and coworkers, as well as the physical health of coworkers. Similarly, stay at work programs were seen as valuable for mental health and morale of coworkers. There was no relationship between perceived influence of DM interventions and reduction of sickness absence. The influence of DM was perceived as more positive for private and/or nonunionised workplaces. No gender differences were evident.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.493
Teacher spread0.419 · 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.

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

Citations5
Published2017
Admission routes3
Has abstractyes

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