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Trust, Workload, Outdoor Adventure Leadership, and Organizational Safety Climate

2021· article· en· W3211071403 on OpenAlexaff
Jeff Jackson, Nevin J. Harper, Scott McLean

Bibliographic record

VenueJournal of Outdoor Recreation Education and Leadership · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of VictoriaAlgonquin College
Fundersnot available
KeywordsOrganisation climateOrganizational safetyWorkloadPsychologyEnvironmental resource managementApplied psychologyPublic relationsOrganizational commitmentSocial psychologyManagementPolitical scienceOrganizational behavior and human resourcesEnvironmental science

Abstract

fetched live from OpenAlex

The outdoor adventure leadership (OAL) field has an extensive body of work centered on individual safety performance, but much less at the organization level of analysis and assessment of organizational safety. Safety climate is a well-established construct and when measured can be indicative of employees’ perceptions of organizational safety and predictive of safety performance. This study employed a safety climate scale and surveyed 506 employees across ten United States OAL not-for-profit organizations. Dimensions of safety as a recognized value, and leadership and management for safety typically scored the highest across organizations. The Dimensions of safety as learning oriented, and safety as integrated into operations, typically scored the lowest. Trust in the organization and OAL delivery pressure, workload, and stress emerged as important indicators of safety climate at the organizational level. Directions for future research based upon this safety climate tool are identified.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.166
GPT teacher head0.436
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes1
Has abstractyes

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