Human factors in the field: a field study of accident investigation at the transportation safety board of Canada
Bibliographic record
Abstract
The goals of this research are twofold. First in the sense of direct applicability to investigative bodies, the goal is to identify the cognitive strategies used by accident investigators to uncover human factors issues and to actively manage accident information. This information is pertinent to training and job aid design. Second, in an academic sense, the goal is to identify and conceptualize the factors affecting information management in a complex work environment. The field study is based on grounded theory and involved participant observation carried out over a three and a half year period, in addition to the documentary review of investigation records. Three major classes of strategies employed by accident investigators to manage information were revealed; information minimising, information creation, and information balancing. The field study also identified a number of constraints affecting the use and flow of information during the investigation process. Methods used in this study are demonstrated as useful tools for researchers examining cognitive activities, analyzing patterns of behaviour, and the management of information between the various interacting players in a complex, real world, work system. Results of the field study lead to the development of a conceptual model of information management called “the funnels of investigation and the investigation trajectory”. The model captures three levels of constraints imposed on information. Information is constrained: (a) by the organization, (b) by contextual factors and investigator perspective, and (c) through the control of information by accident investigators as they employ information management strategies and causal interpretations to the information. The proposed model of information management provides a framework to help direct future research in this area. The conceptual model proposed highlights the times during the investigation process when information is constrained, and suggests where efforts can be placed to reduce those constraints. The information management strategies identified, particularly those that create information, can assist in broadening the information observable by accident investigators. A number of prescriptive recommendations are made to improve investigator training. These findings should be of use to those developing training programs or support tools for practitioners.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".