Thematic Analysis of Key Recommendations from Commissioned Occupational Health and Safety Reports in Mining
Bibliographic record
Abstract
The objective of the study was to address the recommendations from 10 commissioned occupational health and safety (OHS) reports from the mining industry internationally, spanning the past 50 years. The investigation involved a two-step thematic analysis using Leximancer, a text mining software, to identify the key themes and concepts present in the recommendations. First, Leximancer was utilized to analyze the manifest content of each report through conceptual and relational analysis to produce concept maps. The Leximancer mapping subsystem works in two stages, characterized as semantic extraction of dominant themes, followed by relational extraction [1]. Next, a seeded analysis of the term safety culture was conducted to determine how the concept of safety culture overlaps or diverges from the discussion and recommendations present in the documents [2]. It is evident from the initial analysis that although safety culture was discussed briefly in a few of the documents, it was not a consideration in the formation of the recommendations. Therefore, as results indicate, if the recommendations continue to focus on engineering more solutions for past errors, instead of focusing on the organizations safety culture, they will fail to prevent accidents and fatalities of the future. Applying the findings of this research to OHS in mining, and other industries, such as health care, construction and aviation, has the potential to provide a greater contribution to the prevention of occupational accidents and risk reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".