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The Shelf Life of a Safety Climate Assessment: How Long Until the Relationship with Safety-Critical Incidents Expires?

Bergman ME, Payne SC, Taylor AB, Beus JM - J Bus Psychol (2014)

Bottom Line: The common yearly count of incidents would make it seem that more severe incidents cannot be predicted by safety climate and also fails to show the strongest predictive effects of less severe incidents.This research is the first to examine assumptions regarding aggregation periods when constructing safety-related incident rates.Our work guides organizations in planning their survey program, recommending more frequent measurement of safety climate.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Texas A&M University, College Station, TX USA.

ABSTRACT

Purpose: This study investigates safety climate as both a leading (climate → incident) and a lagging (incident → climate) indicator of safety-critical incidents. This study examines the "shelf life" of a safety climate assessment and its relationships with incidents, both past and future, by examining series of incident rates in order to determine when these predictive relationships expire.

Design/methodology/approach: A survey was conducted at a large, multinational chemical manufacturing company, with 7,467 responses at 42 worksites in 12 countries linked to over 14,000 incident records during the 2 years prior and 2 years following the survey period. Regressions revealed that safety climate predicts incidents of varying levels of severity, but it predicts the most severe incidents over the shortest period of time. The same is true for incidents predicting safety climate, with more severe incidents having a shorter predictive window. For the most critical relationship (climate predicting more severe incidents), the ability of a safety climate assessment to predict incidents expires after 3 months.

Implications: The choice of aggregation period in constructing incident rates is essential in understanding the safety climate-incident relationship. The common yearly count of incidents would make it seem that more severe incidents cannot be predicted by safety climate and also fails to show the strongest predictive effects of less severe incidents.

Originality/value: This research is the first to examine assumptions regarding aggregation periods when constructing safety-related incident rates. Our work guides organizations in planning their survey program, recommending more frequent measurement of safety climate.

No MeSH data available.


Related in: MedlinePlus

Graphical representation of the semipartial r2 for safety climate assessment as a predictor of later safety incidents (i.e., safety climate as a leading indicator)
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Fig2: Graphical representation of the semipartial r2 for safety climate assessment as a predictor of later safety incidents (i.e., safety climate as a leading indicator)

Mentions: Table 4 contains the sr2 and unstandardized regression coefficients; Fig. 2 displays the sr2 graphically. We focus on sr2, rather than regression coefficients, because our interest is in the variance accounted for by safety climate as a predictor of incident rates. As seen in Table 4, very few of the sr2 were 0.01 or higher in magnitude for Learning Events or Near Misses, with prediction of only the 5- and 6-month Learning Event variables exhibiting a sr2 greater than 0.01. Although these values are in the range of small effects (Cohen 1988), it is difficult to discern why these two dependent variable time periods, and no others, were predictable. Thus, our conclusion is that site-level safety climate assessment is unable to predict the rate of Learning Events or Near Misses over a 2-year period.Table 4


The Shelf Life of a Safety Climate Assessment: How Long Until the Relationship with Safety-Critical Incidents Expires?

Bergman ME, Payne SC, Taylor AB, Beus JM - J Bus Psychol (2014)

Graphical representation of the semipartial r2 for safety climate assessment as a predictor of later safety incidents (i.e., safety climate as a leading indicator)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4232754&req=5

Fig2: Graphical representation of the semipartial r2 for safety climate assessment as a predictor of later safety incidents (i.e., safety climate as a leading indicator)
Mentions: Table 4 contains the sr2 and unstandardized regression coefficients; Fig. 2 displays the sr2 graphically. We focus on sr2, rather than regression coefficients, because our interest is in the variance accounted for by safety climate as a predictor of incident rates. As seen in Table 4, very few of the sr2 were 0.01 or higher in magnitude for Learning Events or Near Misses, with prediction of only the 5- and 6-month Learning Event variables exhibiting a sr2 greater than 0.01. Although these values are in the range of small effects (Cohen 1988), it is difficult to discern why these two dependent variable time periods, and no others, were predictable. Thus, our conclusion is that site-level safety climate assessment is unable to predict the rate of Learning Events or Near Misses over a 2-year period.Table 4

Bottom Line: The common yearly count of incidents would make it seem that more severe incidents cannot be predicted by safety climate and also fails to show the strongest predictive effects of less severe incidents.This research is the first to examine assumptions regarding aggregation periods when constructing safety-related incident rates.Our work guides organizations in planning their survey program, recommending more frequent measurement of safety climate.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Texas A&M University, College Station, TX USA.

ABSTRACT

Purpose: This study investigates safety climate as both a leading (climate → incident) and a lagging (incident → climate) indicator of safety-critical incidents. This study examines the "shelf life" of a safety climate assessment and its relationships with incidents, both past and future, by examining series of incident rates in order to determine when these predictive relationships expire.

Design/methodology/approach: A survey was conducted at a large, multinational chemical manufacturing company, with 7,467 responses at 42 worksites in 12 countries linked to over 14,000 incident records during the 2 years prior and 2 years following the survey period. Regressions revealed that safety climate predicts incidents of varying levels of severity, but it predicts the most severe incidents over the shortest period of time. The same is true for incidents predicting safety climate, with more severe incidents having a shorter predictive window. For the most critical relationship (climate predicting more severe incidents), the ability of a safety climate assessment to predict incidents expires after 3 months.

Implications: The choice of aggregation period in constructing incident rates is essential in understanding the safety climate-incident relationship. The common yearly count of incidents would make it seem that more severe incidents cannot be predicted by safety climate and also fails to show the strongest predictive effects of less severe incidents.

Originality/value: This research is the first to examine assumptions regarding aggregation periods when constructing safety-related incident rates. Our work guides organizations in planning their survey program, recommending more frequent measurement of safety climate.

No MeSH data available.


Related in: MedlinePlus