Unrealistic Optimism, Fatalism, and Risk-Taking in New Zealand Farmers’ Descriptions of Quad-Bike Incidents: A Directed Qualitative Content Analysis
Bibliographic record
Abstract
Quad-bike incidents are a major cause of occupational injury and fatality on farms warranting health and safety attention. As part of a larger study, we carried out a face-to-face survey with 216 farmers in New Zealand. We quantitatively identified farmers' propensity for risk-taking, unrealistic optimism, and fatalism as risk factors in quad-bike loss-of-control events (LCEs). The purpose of the analysis presented in this article was to use these same farmers' recollections of LCEs to explore the a priori constructs in more detail using qualitative methods. Participants reporting one or more LCEs described their first LCE and any experienced in the previous 12 months. Participants provided open-text responses about what occurred at each LCE, their reflections, and general thoughts on LCE risk factors. Directed qualitative content analysis (QCA) was used to "unpack" risk-taking, unrealistic optimism, and fatalism whilst also delineating any additional concepts that farmers associate with LCEs. Risk-taking elements were more evident than unrealistic optimism or fatalism and more suggestive of farmers finding themselves in risky situations rather than engaging in risk-seeking behavior per se. Additional inductively derived categories of fatigue/stress, multitasking, inexperience, and quad-bike faults highlight the complex nature of LCEs and the importance of risk assessment covering these concepts as well as risky situations.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".