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Record W4394689743 · doi:10.1097/jom.0000000000003112

Feasibility of a Capacity Building Organizational Intervention for Worker Safety and Well-being in the Transportation Industry

2024· article· en· W4394689743 on OpenAlexaff
Susan E. Peters, María-Andrée López Gómez, Gesele Hendersen, Marta Martínez Maldonado, Jack T. Dennerlein

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIntervention (counseling)UnrestCapacity buildingAgile software developmentBusinessPublic relationsPoliticsEconomic growthPolitical scienceManagementMedicineNursingEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: This study developed, implemented, and evaluated the feasibility of executing an organizational capacity building intervention to improve bus driver safety and well-being in a Chilean transportation company. Method: Through an implementation science lens and using a pre-experimental mixed methods study design, we assessed the feasibility of implementing a participatory organizational intervention designed to build organizational capacity. Result: We identified contextual factors that influenced the intervention mechanisms and intervention implementation and describe how the company adapted the approach for unexpected external factors during the COVID-19 pandemic and social and political unrest experienced in Chile. Conclusions: The intervention enabled the organization to create an agile organizational infrastructure that provided the organization's leadership with new ways to be nimbler and more responsive to workers' safety and well-being needs and was robust in responding to strong external forces that were undermining worker safety and well-being.

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)0.000
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 score0.009
Threshold uncertainty score0.162

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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