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Record W2946136430 · doi:10.3233/wor-192910

Insurers’ perspective on barriers and facilitators for return to work after occupational injuries

2019· article· en· W2946136430 on OpenAlexaffabout
M. Robichaud, Manon Truchon, Louise St-Arnaud, Iuliana Nastasia

Bibliographic record

VenueWork · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailCentre for Interdisciplinary Research in RehabilitationUniversité Laval
Fundersnot available
KeywordsThematic analysisContext (archaeology)Workers' compensationPerspective (graphical)Work (physics)Compensation (psychology)BusinessPublic relationsActuarial scienceQualitative researchPsychologySocial psychologySociologyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the importance of the legislative and insurance systems in the return to work process after an occupational injury, the perspective of the insurer on what influences return to work has rarely been documented. OBJECTIVE: To understand the barriers or facilitators for return to work, from the perspective of the insurer. METHODS: A comprehensive qualitative approach was used. Semi-directed interviews were done with nineteen (19) insurers (claims adjudicator and rehabilitation case manager) from a Canadian workers' compensation board. A thematic analysis was done using QDA Minor Software. RESULTS: Fourteen themes (e.g. family reaction, quality of work relationship) were classified into four categories representing the main stakeholders: worker with disability, workplace, healthcare system and compensation system. Emotional, cognitive, and adaptive reactions from the worker and his family were identified. We observed that good work relations and support practices, lack of access to medical resources, focus on the employee's ability, and complexity and consequences of the compensation process are the main barriers and facilitators from the insurers' perspective. Many of the perceived elements are coherent with the compensation system's administrative and legal context. CONCLUSIONS: The results enable us to better understand the insurers' perspective regarding what influences return to work. It reinforces the necessity to consider the administrative and legal context to better understand the insurers' perspective.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.441
Teacher spread0.404 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2019
Admission routes2
Has abstractyes

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