journal of safety research measuring determinants of occupational health related behavior in flemish...
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Journal of Safety Research
Measuring determinants of occupational health related behavior in Flemish farmers: An application of
the Theory of Planned Behavior
(A.Colémont , S. Van den Broucke)*Catholic University Leuven
*research Group on Health Psychology, Onderzoekseenheid Psychologie, Leuven, Belgiumfarmer.pdf
Introduction
Agriculture is generally recognized as a hazardous occupation, as shown by the high number of occupational accidents and health problems occurring in this sector.
In the United States, agriculture, forestry and fishery accounted for 14.3% of the fatal occupational accidents, 2% of the non–fatal occupational injuries, and 2.3% of the occupational diseases in 2002 (U.S. Department of Labor, 2004).
In Europe, agriculture is the fourth most hazardous occupational sector, with a mortality rate among agricultural workers of 12.4/100,000 in 1998 (European Agency for Safety and Health at Work, 2004).
Comparable rates have been reported for Canada, with a mortality rate among farmers of 11.6/100,000 and an injury rate of 177.8/100,000 between 1990 and 2000 (CAISP, 2003), and for Australia, with a fatality rate of 8.9/100,000 between 1985 and 1996 (Day,1999).
Introductionit is important to identify the risk factors that cause the abovementioned hazards to lead to accidents and diseases. In this regard, a distinction can be made between demographic, environmental, and behavioral risk factors.
Introduction
these behaviors include jumping off a tractor before it has come to a complete standstill, repairing machines before they have stopped running, using machinery for the wrong purposes, bad maintenance of machines, overcharging machines, and allowing children to approachthem.
IntroductionReducing these practices, using protective.
use can be made of behavioral models to investigate the determinants of health– and safety–related behavior among farmer
Introduction
One of the most model of preventive behavior change is the Theory of Planned Behavior (TPB; Ajzen & Fishbein, 1974)
This model states that people's health–related behavior is based This behavioral intention is in itself influenced by attitudes subjective norms and perceived behavioral control
Introduction
The TPB has been extensively applied to health–related problems such as smoking prevention, alcohol consumption,…the application of the TPB to preventive behavior change in the field of agriculture is very limited
objectivesThe present study aimed to contribute to the study of the determinants of occupational health and safety–related behaviors of farmersdeveloping and validating a questionnaire based on the TPB to assess attitudes, social norms, perceived behavioral control, intended behavior, and self– reported behavior determinants of 4 important risk behaviors related to farming: working with machinery, handling animals, preventing falls, and using pesticides
Method
Procedure: To obtain a sample of Flemish farmers, use was made of a data file containing VAT–registered businesses obtained from the Federal Public Service Finance to draw a stratified sample of 750 farmersEach farmer in the sample received a copy of the questionnaire by mail, together with an accompanying letter instructions, and a stampedenvelope for return delivery
Method
Since the farming population is known for its low participation in surveys the possibility of winning a 25€ coupon was offered to all participants as an incentive. In addition, a reminder was sent after 4 weeks.
Sample
283(39%)completed questionnaires:age ranged from 24 to 84 years, education level ranged from primary school (16%) and lower secondary school (35%) to higher secondary school (30%) and higher education of 3 years (9%) or 4 years (8%), (45%) worked on a farm focusing on a single activity, dairy farms (8%), cattle (10%), pig confinement (1.5%), poultry farms (1%), crop–growing (9%), fruit growing (8%), horticulture (6%), and floriculture (3%).The remaining 55% worked on farms in which severalactivities were combined
Instrument
The provisional questionnaire that the participants received contained 134 self–report items measuring the 4 risk related behaviors (machinery use, animal handling, fall prevention, and pesticide use)88 items were formulated to measure the behaviors, and 47 to measure the behavioral determinants
Instrument
Machinery use: 4 items were formulated to
measure attitude, I find it important to always shieldthe power take–off’),
• 4 items measured subjective norms: Most people I know disapprove that others ride along on a tractor
• 4 items measured perceived behavioral control: 'It depends on me whether I am visible for others on the road
• 11 items measured intention: I intend to keep the manual of all my machines
• 11items measured reported behavior: I never jump off atractor before complete standstill
Instrument
Animal handling:4 items measured attitude,It is safer to keep small children away from the animals
• 5 items measured subjective norms: My general practitioner expects that I get my vaccinations in time
• 4 items measured perceived behavioral control: It depends on me whether I wear safety shoes while I work with animals
• 12 items measured intention: I try to keep small children away from the animals
• 12 items measured reported behavior: 'I make sure thestables are ventilated properly
Instrument
Fall prevention:3 items measured attitude, It is annoying to clean dirty floors immediately
• 4 items measured subjective norms: others farmers make sure the floors are dry
• 3 items measured perceived behavioral control: I decide whether I carefully stack my tools or not
• 10 items measured intention: I always try to secure myself when I walk on the hayloft
• 10 items measured reported behavior: I haveboth my hands free when I climb a ladder
Instrumentpesticide use:3 items measured attitude, Thoroughly reading instructions when using pesticides is time–consuming• 6 items measured subjective norms: most people find that you have to keep
pesticides in their original package
• 2 items measured perceived behavioral control: I am responsible for the protection of my eyes when I work with pesticides
• 11 items measured intention: I try to wear protectiveclothing or use a hermetically sealed tractor when I use pesticides
11 items measured reported behavior: I stock pesticides in a separate room All items were phrased as statements to be rated on a 5–pointLikert scale (1=strongly disagree, 2=disagree, 3=no opinion,4=agree, 5=strongly agree). In addition, a number of itemswere included asking for personal and demographic characteristics and professional activity.
Instrument
All items were phrased as statements to be rated on a 5–pointLikert scale (1=strongly disagree, 2=disagree, 3=no opinion,4=agree, 5=strongly agree). In addition, a number of itemswere included asking for personal and demographic characteristics and professional activity.
Statistical analysesThese were (a) items with 95% or more of the given answers in the same category, and (b) items with a standard deviation lower than .75The extent to which the theoretical dimensions of the TPB (attitude, subjective norm, and perceived behavioral control) could be reproduced was examined by performing a Confirmatory Factor Analysis • For each behavior, the 3–factor model was tested with
Amos 6.
Statistical analysesIn addition to chi–square, use was made of chi–square divided by the degrees of freedom, which is less sensitive to sample size
root mean square error of approximation (RMSEA), the goodness of fit index (GFI), the adjusted goodness of fit index (AGFI; adjusted for degrees of freedom), and the comparative fit index (CFI) RMSEA<.05good,.05<on<.08acceptable,>.08reasonableerrors of approximation in the population
GFI, AGFI, CFI >0. 90pca.docx
Statistical analysesPrincipal Component Analyses (PCA) were performed to examine the underlying structure of the questionnairepca.docx The internal consistency of the scales obtained through the PCA was tested by means of the Cronbach alphaTo control for the length of the scales, the Spearman Brown prophecy formula was appliedPearson correlations were computed between the scales for each behavior
Statistical analysesmultiple regression analyses were applied to evaluate whether attitude, subjective norm, and perceived behavioral control
ResultsItem analysis: a low discriminative power was found for 34 behavior and intention items: 9 for machinery use, 12 for animal handling, 4 for fall prevention, and 9 for pesticide use. These items were discarded for further analysis
Confirmatory factor analysis• self–reported behavior: CFA resulted in acceptable goodness of
fit indices for pesticide use only RMSEA=.07, CFI=.96, GFI=.96, and AGFI=.93 other scales, mixed findings were obtained for the various
• indices: for animal handling:GFI=.97 and AGFI=.91
• for machinery use CFI=.91, GFI=.95, and AGFI=.90
• For fall prevention:RMSEA=.13, CFI=.89, GFI=.88, AGFI=.80
all fit indices were clearly insufficient• behavioral intention, acceptable levels of the GFI
were obtained for all four the scales
Exploratory factor analysis
behavioral and behavioral intention:Machinery:For the items measuring determinants of machinery use,
removing 3 items with a low factor
animal handling:Removal of 1 behavior and one intention item with
a low component, elimination of 3 items with low factor(PCA)
• Reliability analysis:(Cronbach alpha) ranged from .25 (for perceived control with regard to machinery use) to .89 (for fall prevention behavior) perceived control lower internal consistency levels
• This is probably due to the low number of items per scale.
Reliability analysis
• As Chronbach alpha is very sensitive to a small number of items(reasonably homogenous)
• Spearman Brown prophecy formula, whereby for each scale internal consistency was computed for a hypothetical scale length of 9 items
• the real value of alpha is also very low (.25)perceived behavioral control of machinery Therefore, it was decided to not use this scale for further analysis and instead to use the individual items
Reliability analysis
Inter–scale correlations:Table 2.farmer.pdf• For all 4 the behaviors, strong correlations are
however observed between behavioral intention and behavior Perceived control shows a moderate correlation with behavior for pesticide use only
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Multiple regression analysisMultiple regression Table 3
• Machinery use:attitude, 38% of the variance, but negative subjective norm and the two other perceived behavioral control
animal handling:45% of the variance Perceived behavioral control turned was not a significant
Multiple regression analysis
fall prevention:40% of the variance in intention. while perceived behavioral control failed to reach significance • Pesticide use: 51% of the variance When
looking at the prediction of self–reported behavior,
Discussion• TPB via a CFA could not be achieved, the subscales of
the questionnaire resulting from this analysis strongly. the fact that these dimensions were not directly confirmed by the confirmatory factor analysis is probably
• because these scales were theoretically derived, rather than replicating existing scales(12 subscales measuring)
Discussion
• internal consistency: low internal consistency levels may be due to their relatively low level of items (After using a Spearman–Brown correction, most subscales reach a good level of internal consistency).It may be due to its items measuring very different aspects of machinery use(other studies)
• Multiple regression analyses:
perceived behavioral control did not contribute to the prediction
of the latter 3 behaviors. However, the less significant contribution of
perceived control to behavior and behavioral intention is in line with other studies in which the TPB was applied to agricultural settings((Petrea, 2001; Lee et al., 1997).
Discussion
A significant percentage of the variance of intentions and behaviors among farmers is explained by the components of the modelOverall, the current study demonstrated the validity of theTheory of Planned Behavior in predicting behavior related tooccupational safety and health among farmers, and produced a valid and reliable questionnaire to measure the cognitiveconcepts featured in this model
Discussion
By making use of the knowledge regarding the contribution of behavioral determinants to specific behaviors, interventions can be developed that better suit the needs of the target group and maytherefore be more effective than the existing programs
ReferencesReferencesAjzen, I. (1991). The theory of planned behavior. Organizational Behaviorand Human Decision Processes, 50, 179−211.Ajzen, I., & Fishbein, M. (1974). Understanding Attitudes and PredictingSocial Behavior. Englewood Cliffs, NJ: Prentice Hall, Inc..Ajzen, I., & Madden, T. J. (1986). Prediction of goal directed behavior:attitudes, intentions and perceived behavioral control. Journal ofExperimental Social Psychology, 22, 453−474.Alavanja, M. C., Sprince, N. L., Oliver, E., Whitten, P., Lynch, C. F., Gillette,P. P., Logsden–Sacket, N., & Zwerling, C. (2001). Nested case–controlanalysis of high pesticide exposure events from the Agricultural HealthStudy. American Journal of Industrial Medicine, 39(6), 557−563.Armitage, C. J.,&Conner,M. (2001). Efficacy of the theory of planned behavior:a meta–analytic review. British Journal of Social Psychology, 40, 471−499.Baris, D., Zahm, S. H., Cantor, K. P., & Blair, A. (1998). Agricultural use ofDDTand risk of non Hodgkin's lymphoma: pooled analysis of three casecontrol studies in the United States. Occupational and EnvironmentalMedicine, 55(8), 522−527.Bentler, P. M (1992). On the fit of models to covariances and methodologyto the Bulletin. Psychological Bulletin, 112, 400−404.Bollen, K. A., & Long, J. S. (1993). Introduction. In K. A., & J. S. (Eds.),Testing structural equation models (pp. 1−9). Newbury Park, CA: Sage.Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing modelfit. Sociological Methods and Research, 21, 230−258.Browning, S. R., Truszczynska, H., Reed, D., & McKnight, R. H. (1998).
ReferencesAgricultural injuries among older Kentucky farmers: The Farm FamilyHealth and Hazard Surveillance Study. American Journal of IndustrialMedicine, 33(4), 341−353.CAISP (2003). Agricultural injuries in Canada for 1990−2000. Kingston,Ontario: Queen’s University, Canadian Agricultural Injury SurveillanceProgram. (URL http://meds.queensu.ca/~emresrch/caisp/natrep.html[2006, February 20].Colémont, A., & Van den Broucke, S. (2006). Psychological determinants ofbehaviors leading to occupational injuries and diseases in agriculture: a
36
References• literature overview. Journal of Agricultural Safety and Health, 12(3),• 227−238.• Criddle, L. M. (2001). Livestock trauma in central Texas: cowboys,• ranchers, and dudes. Journal of Emergency Nursing, 27(2), 132−140.• Day, L. M. (1999). Farm work related fatalities among adults in Victoria,• Australia: the human cost of agriculture. Accident Analysis and Prevention,• 31(1 2), 153−159.• DeRoo, L. A., & Rautiainen, R. H. (2000). A systematic review of farm safety• interventions. American Journal of Preventive Medicine, 18(4 Suppl), 51−62.• Dich, J., & Wiklund, K. (1998). Prostate cancer in pesticide applicators in• Swedish agriculture. Prostate, 34(2), 100−112.• ESAW. (2004), (URL http://www.av.se/statistik/dok/0000202.pdf. [2004,• January 29].• Godin, G., & Kok, G. (1996). The theory of planned behavior: a review of its• applications to health related behaviors. American Journal of Health• Promotion, 11, 87−98.
ReferencesHorsburgh, S., Feyer, A.M., & Langley, J. D. (2001). Fatal work related injuriesin agricultural production and services to agriculture sectors of New Zealand,1985 94. Occupational and Environmental Medicine, 58(8), 489−495.Hu, L. T., &Bentler, P.M(1995). Evaluatingmodel fit. In R. H. (Ed.), Structuralequation modelling: Concepts, issues and applications (pp. 77−99).Thousand Oaks: Sage.Hwang, S. A., Gomez,M. I., Stark, A. D., St Joh, T. L.,May, J. J., & Hallman,E. M. (2001). Severe farm injuries among New York farmers. AmericanJournal of Industrial Medicine, 40(1), 32−41.Lee, B. C., Jenkins, L. S., & Westaby, J. D. (1997). Factors influencingexposure of children to major hazards on family farms. Journal of RuralHealth, 13(3), 206−215.Lewis,M. Q., Sprince, N. L., Burmeister, L. F., Whitten, P. S., Torner, J. C., &Zwerling, C. (1998).Work–related injuries among Iowa farmoperators: ananalysis of the Iowa Farm Family Health and Hazard Surveillance Project.American Journal for Industrial Medicine, 33(5), 510−517.Lilley, R., Feyer, A. M., Kirk, P., & Gander, P. (2002). A survey of forestworkers in New Zealand. Do hours of work, rest, and recovery play arole in accidents and injury? Journal of Safety Research, 33(1), 53−71.Loomis, D. P., Richardson, D. B.,Wolf, S. H., Runyan, C.W., & Butts, J. D.(1997). Fatal occupational injuries in a southern state. American Journalof Epidemiology, 145(12), 1089−1099.
37
38
ReferencesPetrea, R. E. (2001). The theory of planned behavior: use and application intargeting agricultural safety and health interventions. Journal ofAgricultural Safety and Health, 7(1), 7−19.Pickett,W., Brison, R. J., Niezgoda, H., & Chipman, M. L. (1995). Nonfatalfarm injuries in Ontario: a population based survey. Accident Analysisand Prevention, 27(4), 425−433.• Rasmussen, K., Carstensen, O., & Lauritsen, J. M. (2000). Incidence of• unintentional injuries in farming based on one year ofweekly registration in• Danish farms. American Journal of Industrial Medicine, 38(1), 82−89.• Reynaldo, J., & Santos, A (1999). Cronbach's alpha: A tool for assessing the• reliability of scales. Journal of Extension, 37(2) Available online: www.• joe.org/goe/1999april/tt3.html• Reynolds, S. J., & Groves, W. (2000). Effectiveness of roll over protective• structures in reducing farm tractor fatalities. American Journal of• Preventive Medicine, 18(4 Suppl), 63−69.• Stallones, L., & Xiang, H. (2003). Alcohol consumption patterns and work• related injuries among Colorado farm residents. American Journal of• Preventive Medicine, 25(1), 25−30.• Susitaival, P., & Hannuksela, M. (1995). The 12 year prognosis of hand• dermatosis in 896 Finnish farmers. Contact Dermatitis, 32(4), 233−237.
ReferencesSutton, S. (1998). Explaining and predicting intentions and behavior:Howwellare we doing? Journal of Applied Social Psychology, 28, 1318−1339.Thelin, A., Jansson, B., Jacobsson, B., & Strom, H. (1997). Coxarthrosis andfarm work: a case referent study. American Journal of IndustrialMedicine, 32(5), 497−501.U.S. Department of Labor. (2004). (URL http://www.bls.gov [2004, January 29].Wiggins, J. S. (1973). Personality and prediction. Reading: AddisonWesley.Young, S. K. (1995). Agriculture related injuries in the parkland region ofManitoba. Canadian Family Physician, 41, 1190−1197.