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Record W1962544418 · doi:10.3233/wor-152141

Policy on professional support in return-to-work: Occupational health professionals’ experiences in a Canadian setting

2015· article· en· W1962544418 on OpenAlexaboutno aff
Karin Maiwald, Agnes Meershoek, Angelique de Rijk, Frans Nijhuis

Bibliographic record

VenueWork · 2015
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsHealth careWork (physics)Occupational safety and healthPerspective (graphical)NursingPsychologyPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada and other countries, sickness-based absences among workers is an economic and sociological problem. Return-to-work (RTW) policy developed by both employer and worker' representatives (that is, bipartite policy) is preferred to tackle this problem. OBJECTIVE: The intent was to examine how this bipartite agreed-upon RTW policy works from the perspective of occupational health professionals (those who deliver RTW services to workers with temporary or permanent disabilities) in a public healthcare organization in Canada. METHODS: In-depth interviews were held with 9 occupational health professionals and transcribed verbatim. A qualitative, social constructivist, analysis was completed. RESULTS: The occupational health professionals experienced four main problems: 1) timing and content of physicians' medical advice cannot be trusted as a basis for RTW plans; 2) legal status of the plans and thus needing workers' consent and managers' approval can create tension, conflict and delays; 3) limited input and thus little fruitful inference in transdisciplinary meetings at the workplace; and yet 4) the professionals can be called to account for plans. CONCLUSIONS: Bipartite representation in developing RTW policy does not entirely delete bottlenecks in executing the policy. Occupational health professionals should be offered more influence and their professionalism needs to be enhanced.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.059
GPT teacher head0.450
Teacher spread0.391 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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