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Record W4400352325 · doi:10.1093/occmed/kqae023.1428

P-605 SUPPORTING EMPLOYERS IN IMPLEMENTING GRADUAL RETURNS TO WORK: A TRANSDIAGNOSTIC AND INCLUSIVE TOOL

2024· article· en· W4400352325 on OpenAlexaff
Marie‐José Durand, Marie‐France Coutu, Chantal Sylvain, Daniel Côté

Bibliographic record

VenueOccupational Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de Sherbrooke
Fundersnot available
KeywordsWork (physics)PsychologyBusinessComputer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Introduction The Tool for Supporting a Gradual Return to Work (TS-GRTW) allows implementation of gradual RTW by facilitating collaboration between employers and workers with MSD-related work disabilities. This tool was adapted for use with workers who have common mental disorders, taking some vulnerability factors into account. The aim is to test the usability of this adapted tool with individuals responsible for disability management in workplaces. Methods A simple descriptive design was retained. Purposive sampling was used to recruit 23 individuals working for at least the previous year in disability management in their workplace. Various types of workplaces were sought. Semi-structured interviews were conducted and transcribed. The verbatim were analyzed based on a qualitative approach.“ Results The participants (6 M, 17 F, mean age 42.6 years, SD 8.1) came from 10 activity sectors, mostly in large companies. Many of them (17/23) saw benefits in using the tool in their organization, as it allows to: 1. standardize the RTW process, 2. delegate tasks to managers and supervisors, 3. structure communication among stakeholders, 4. monitor actions 5. motivate worker engagement, and 6. foster common understanding and legitimization of the process within the organization. A large proportion of participants (17/23) envisaged using the tool. Certain organizational constraints could limit implementation (e.g. lack of time). Discussion Our results support the usability of the adapted TS-GRTW by potential users. However, our results mainly reflect large companies, which often have structures in place. Conclusion The next step would be to test usability with the workers and its implementation.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.100
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.163
GPT teacher head0.491
Teacher spread0.328 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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