Honeycutt Science advances workplace safety through applied research, data visualization, field analytics, and practical models built for real organizations.
Organizations rarely fail because they lack rules. They fail because leadership systems, training systems, and culture signals drift out of alignment with real exposure.
Foreseeable Injury Events Dataset
De-identified U.S. occupational injury events curated for injury mechanism and foreseeability analysis. Published open-source for independent academic review and evaluation by other experts.
Honeycutt, John (2026). De-identified U.S. Occupational Injury Events with Standardized Injury and Industry Classifications.
Mendeley Data, V1.
doi: 10.17632/kj6dbshsnp.1
The Decision Advantageâ„¢
Strategic decision-system analysis provided in collaboration with The Decision Advantage™. This work helps leaders see how decisions are formed, debated, and carried forward—so risk is easier to anticipate.
This work highlights how weak decision systems can increase cost and reduce performance, supported by applied research and decision analytics.
Standardized Research Datasets
These four datasets are part of one research project built to make large public government data easier to study and use. Each dataset takes complex source records and reorganizes them into a clear, consistent coding system that can be used across different fields, including transportation, consumer product safety, and workplace injury research. To do this, each record is translated into a standard letter-and-number code format called AAA_ZZZZZ, which helps turn mixed and inconsistent source terms into simple, readable categories.
This process helps organize raw information into groups that are easier to compare, sort, and study. Instead of leaving the data in many different forms, the system places similar events into shared categories using the same coding rules across all four datasets. This makes the data more useful for research, pattern finding, case review, and future analysis, while still keeping the original information understandable and consistent.
APA Citations
- Honeycutt, J. (2026). De-identified U.S. fatal crash events with standardized operational, environmental, and participant classifications (2004, 2014, 2024) (Version 1) [Data set; derived from National Highway Traffic Safety Administration Fatality Analysis Reporting System (FARS)]. Mendeley Data. https://doi.org/10.17632/x3dy25xk3p.1
- Honeycutt, J. (2026). De-identified U.S. truck crash events with standardized operational and environmental classifications (Version 1) [Data set; derived from U.S. Department of Transportation Federal Motor Carrier Safety Administration Motor Carrier Management Information System (MCMIS)]. Mendeley Data. https://doi.org/10.17632/7rwjnhkz8d.1
- Honeycutt, J. (2026). De-identified U.S. consumer product incident events with standardized product failure and injury classifications (Version 1) [Data set; derived from U.S. Consumer Product Safety Commission National Electronic Injury Surveillance System (NEISS)]. Mendeley Data. https://doi.org/10.17632/w2gtcmsv2h.1
- Honeycutt, J. (2026). De-identified U.S. occupational injury events with standardized injury and industry classifications (Version 1) [Data set; derived from U.S. Bureau of Labor Statistics Survey of Occupational Injuries and Illnesses (SOII)]. Mendeley Data. https://doi.org/10.17632/kj6dbshsnp.1
Communication (CEC)
Change Enabling Communication (CEC) treats communication as an operating system—not an announcement.
- Awareness — early, unambiguous signal
- Self-Concern — personal impact addressed directly
- Mental Try-Out — conceptual testing before commitment
- Involvement — participation and reinforcement
- Acceptance — stable operational alignment
The model integrates classic communication theory with staged concern progression to reduce resistance and improve execution reliability.
| Evaluation Category | D & C Characteristics | B & A Characteristics |
|---|---|---|
| Reporting Climate e.g., hotline data, HR records, safety reports | Reporting may occur but is uneven, delayed, or influenced by perceived consequences or informal norms. | Reporting is encouraged, timely, and trusted. Information flows upward without fear of retaliation. |
| Leadership Alignment e.g., budgets, staffing decisions, follow-up actions | Safety priorities may be stated but inconsistently supported by resource allocation or visible leadership behavior. | Leadership actions consistently reinforce stated priorities, including allocation of time, staffing, and resources. |
| Corrective Action Reliability e.g., CAPA records, closure documentation | Corrective actions may be identified but not consistently completed or verified for effectiveness. | Corrective actions are tracked, completed, and verified, with learning applied to prevent recurrence. |
| Hazard Recurrence Patterns e.g., trend analyses, incident history | Similar hazards or event types may recur over time without systematic escalation or redesign. | Recurrent patterns trigger proactive review, system-level correction, and monitoring. |
Safety Culture Model (DCBA)
- D — Reactive, unstable
- C — Compliant, inconsistent
- B — Proactive, strengthening
- A — Advanced, self-correcting
DCBA evaluates how leadership systems perform under real operating pressure.
| Evaluation Focus | D & C Indicators (Reactive / Inconsistent) | B & A Indicators (Proactive / Self-Correcting) |
|---|---|---|
| Risk Communication | Risk information appears fragmented, informal, or inconsistently delivered. Examples may include toolbox talks, safety alerts, or postings that are uneven, delayed, or not clearly tied to task exposure. | Risk information is communicated deliberately and repeatedly in connection with
actual work conditions. Examples may include coordinated briefings, task-specific messaging, and reinforcement through multiple channels aligned with exposure. |
| Message Consistency | Safety expectations vary across leadership or supervisory levels. Examples may include differing supervisor directives, mixed priorities, or inconsistent emphasis between written policy and field practice. | Safety expectations remain consistent across leadership levels and operational roles. Examples may include aligned supervisor guidance, consistent reinforcement, and leadership messaging that matches observed practice. |
| Understanding & Reinforcement | Understanding is assumed rather than confirmed. Examples may include attendance records or acknowledgments without evidence of comprehension or follow-up. | Understanding is actively reinforced and checked over time. Examples may include coaching conversations, scenario discussions, or verification tied to real task execution. |
| Corrective Follow-Through | Corrective actions are documented but closure or verification is inconsistent. Examples may include open items, delayed responses, or repeat issues without clear resolution. | Corrective actions are tracked, completed, and reviewed for effectiveness. Examples may include documented closure, trend monitoring, and adjustment when controls do not perform as intended. |
| Pattern Persistence | Similar issues recur across time or locations. Examples may include repeat hazards, similar incidents, or unresolved themes appearing in reports or logs. | Emerging patterns are identified and addressed before recurrence. Examples may include trend analysis, early intervention, and system-level adjustments to prevent repetition. |