Case study: maximising return on health, safety, environment, and quality (HSE&Q) investments
Bibliographic record
Abstract
Farstad Shipping operates a fleet of large anchor handling and platform supply vessels globally. Priding itself on its motto Better By Far, Farstad Shipping’s vision of zero harm faced key safety challenges, including:leadership/communications;manual task-related injuries;fly in/fly out fatigue; mental health issues relating to stress, depression, etc.;an ageing workforce; and,changing/challenging client requirements. These challenges created a high level of indecision and frustration when trying to determine where best to focus time and money with HSE&Q programs to effectively impact operational productivity and safety performance. Beginning in 2007, Farstad Shipping partnered with Intertek Consulting & Training and implemented a framework designed to pinpoint areas of greatest concern. This approach uses research from OECD indicating best practice. Using this approach enabled Farstad to gather objective data on specific behaviours and to formulate targeted action plans that addressed the people, culture, processes, delivery and sustainability challenges identified above. The framework was applied at sea on more than 25 Farstad vessels via onboard coaching, at onshore workshops, and at the Offshore Simulation Centre based in Western Australia. Subsequently, some key achievements included: three-fold reduction in injury frequency; reduction of total recordable case frequency from 14.0 to >5.0; 50% reduction in workcover claims; and zero lost time injuries since September 2012. The methodology used by Farstad, which can be applied to organisations’ approaches to hiring, training, coaching, process design and leadership development, is examined in detail and its application to Farstad’s challenges explored.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".