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Record W3095893026 · doi:10.70010/afzy8167

Assessment of various critical incident management and support protocols for railway employees after a serious incident

2019· article· en· W3095893026 on OpenAlexaboutno aff
Cécile Bardon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIncident managementCritical Incident TechniqueIncident reportComputer scienceRisk analysis (engineering)Computer securityBusiness

Abstract

fetched live from OpenAlex

The Canadian railway industry must regularly deal with critical incidents (CI) involving collisions with people or vehicles. Such incidents may result in serious injuries or even fatalities, in addition to causing mental health problems for the locomotive engineers and conductors involved. Every year, approximately one hundred people in Canada, including some 20 in Québec, lose their lives in collisions with trains. In addition to fatal events, an unknown number of incidents also occur in which people are injured or property is damaged. Most locomotive engineers and train conductors will be exposed to this type of event at least once in their careers. When a critical incident of this nature occurs, they are witnesses, victims, participants and often first responders, all at the same time. A significant proportion of locomotive engineers and train conductors soon return to satisfactory personal and occupational functioning levels and have very few psychological, social or functional after-effects. However, the recovery time after a CI can be quite long, and employees require support during this period. In addition, between 4% and 17% of these employees will experience more serious problems, including depression, acute stress disorder, posttraumatic stress disorder or anxiety. Several clinical approaches are effective in mitigating posttraumatic symptoms, and a great deal of the research has focused on them. In contrast, needs are less well known and there are fewer resources for those who do not suffer posttraumatic stress, but who struggle with major undiagnosed adverse effects. Some studies have examined the critical incident management and support protocols (CIMSPs) implemented by employers, and they often recommend the adoption of practices to reduce the potential impact of critical incidents on employees and to shorten their recovery period. However, although these protocols are based on studies of CI after-effects and employees’ needs, they have not yet undergone empirical evaluation. Assessments of this kind are necessary to determine the key elements of these protocols that have a positive impact on employee recovery and to promote recommendations based on scientific findings. The aim of this project was to assess the CIMSPs that have been implemented in the Canadian rail industry and their impact on the recovery of employees who have been involved in critical incidents and to propose key practices to reduce the adverse effects. Seventy-four locomotive engineers and train conductors who had experienced a CI were recruited to take part in the study. They were interviewed four times over a six-month period. As well, nine managers of train operations (MTOs) who met the same inclusion criteria were interviewed twice over a three-month period. A mixed-method approach was used to analyse the data collected, combining statistical and qualitative analyses to fully understand the relationships between critical incidents, CIMSPs and post-CI recovery. The perceptions, needs and recommendations of MTOs who are responsible for applying protocols and providing support are also presented. The results indicate that existing CIMSPs are implemented partially or unevenly, depending on employer, province and CI type. In CIs without fatalities, for instance, management and support protocols are not completely followed, even when employee health is affected. Iv Assessment of Various CIMSPs for Railway Employees After a Serious Incident IRSST CIs affect employees in a wide variety of ways. The research team was able to establish five distinct recovery trajectories: no adverse effects, adverse effects that disappear within the month following the CI, adverse effects that gradually decline and disappear within the three months following the CI, adverse effects that reach a plateau between one and three months afterwards and then disappear, and adverse effects that are still present after six months. Overall, among two thirds of employees, the adverse effects of a CI dissipate more or less rapidly in the month following the CI, 20% still feel significant effects after three months (course of the plateau and effects that persist after six months), while that proportion falls to 13% after six months. These effects are not negligible and affect employees’ cognition (concentration, rumination, distraction), energy (fatigue, trouble sleeping) and emotions (guilt, grief). They may also interfere with employees’ ability to perform their jobs effectively. Differences in the application of CIMSPs provide opportunities for assessing their role in the post-CI recovery process. The study results show that management protocols can have an effect on the recovery process. The following factors tend to foster an acceleration of the process: the presence of a manager on site; a manager taking charge at the scene of the CI; the various stakeholders showing respect and empathy toward those affected; no pressure on employees to continue working or to return to work before they are ready; demobilization (automatically removing employees from the scene of the CI and taking them home) and granting them recovery leave; a proactive offer of support by the employee assistance program (EAP); a clear procedure for return to work and assessment of the employee’s readiness to resume occupational duties; deferred offer of support if needed; follow-up after return to work and a positive work environment. The study shows that CI management and the support provided by employers are key factors in promoting employee recovery. These are attitudes and actions that employers can act on and that can be applied fairly easily, without entailing prohibitive costs for companies. Employers have less control over other factors, such as social support or the complexity of the CI. Railway companies have protocols that already include most of the actions deemed to be effective. Following those protocols assiduously is a first step toward improving current practices and mitigating the adverse effects of CIs.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.070
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.041
GPT teacher head0.417
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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