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Striving for a Violence “Free” and Healthy Workplace: Insights from Forestry Workers' Perspectives

2020· book-chapter· en· W3109957478 on OpenAlexaboutno aff
Nicole Cvenkel

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsWorkplace violencePublic relationsCorporate governancePerspective (graphical)Employee engagementPolitical scienceEmployee assistanceEmployee resource groupsOrganizational culturePsychologyEmployee researchManagementSuicide preventionPoison controlMedicine

Abstract

fetched live from OpenAlex

Abstract This chapter critically examines the dynamics that exists between workplace violence, employee well-being, and governance as experienced and perceived by employees in the Forestry context. The purpose of this research is to explore what signals the prevalence of workplace violence in the Forestry sector; to understand the consequences of workplace violence; to explore the degree to which workplace violence can be stopped; and how can employers strive for a violence “free” and healthy workplace. This chapter focuses on research into workplace violence in the Forestry sector in British Columbia, Canada. A questionnaire survey, telephone interviews, and focus groups were used to focus on managers, union, and employees' verbal accounts of their own experiences and perceptions of workplace violence. Managers completed 367 questionnaire surveys. The union and employees from across five different organizations also completed the survey that was analyzed. Twenty semi-structured telephone interviews were conducted with each interview lasting 60–75 minutes, tape-recorded, and transcribed verbatim. Two focus groups were the one with 15 managers only and the other with 10 union representatives. Each focus group lasted 45–60 minutes, tape-recorded, and transcribed verbatim. This research adopted an interpretivist approach, which allows a positivist and an interpretivist viewpoint that examines situations to establish the norm by using questionnaires, interviews, and focus groups. The mixed methodology is appropriate for addressing the research aims and provided insight into the lifeworld of participants, providing the opportunity for managers, union, and employees to share their personal experience of workplace violence. Using Interpretative Phenomenological Analysis (IPA) provided insight into the lifeworld of participants, providing the opportunity for employees, managers, and union representative to share their personal experience of workplace violence and its implications for governance, violence prevention, and employee well-being at work. The data revealed that 13 key themes emerged as salient to forestry workers' perspective of workplace violence, the prevalence of violence, consequences of violence, prevention of violence, and how employers can strive toward a violent “free” and healthy workplace. These themes include Stress Management, Mental Health, Leadership Development, Trust, Employee Involvement and Engagement, Communication and Collaboration, Education and Training, Employee Violence Assistance Program, Violence Response Protocol, Respectful Workplace Culture, Job Redesign, Fear of Change, and Employee Appreciation. This research has relevance for employee well-being, leadership, governance, corporate social responsibility, and performance for practitioners and academics alike. The findings and insights from this research can be extrapolated to other organizations inBritish Columbia, Canada, and other parts of the world.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.012
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0020.004
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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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