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Record W4400355471 · doi:10.1093/occmed/kqae023.0065

SS04-03 RETURN TO WORK MANAGEMENT FOLLOWING AN OCCUPATIONAL INJURY

2024· article· en· W4400355471 on OpenAlexaff
Sara Soltani, Rim El Kholti, Pierre Durand, Mathieu Campbell

Bibliographic record

VenueOccupational Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety in Workplaces
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOccupational safety and healthWork (physics)Occupational medicineOccupational injuryWorkers' compensationMedicineInjury preventionMedical emergencyPoison controlPsychologyEngineeringPathologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Introduction It is important to focus on the issue of returning to work following a professional injury and to better understand it. By doing so, it is possible to identify the complexities and difficulties that can prolong the situation of incapacity. Methods For this, the intervention and involvement of several actors in the organization are necessary. The goal of this intervention project is to better understand what influences the return to work within the emergency health corporation, from the worker's perspective. This qualitative project takes the ecological model of disability management as its conceptual model. Semi-structured interviews were conducted for this project with paramedics and heads of departments concerned with returning to work. Subsequently, an analysis of the interviews is conducted to highlight the elements that facilitate and hinder the return to work. Discussion Elements such as the adaptation strategy, the reactions and emotions of the paramedics and their relatives, as well as elements related to the personal life of the worker, emerged. Concerning the work environment, the quality of working relationships between the employee and their manager, the culture of the organization, and the management practices used are the main elements highlighted by the participants. Conclusion The results obtained during these interviews are consistent with the literature and prompt further reflection on exploring the influence on returning to work within the emergency health corporation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.078
GPT teacher head0.494
Teacher spread0.416 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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