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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.

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Presentation on theme: "Journal of Safety Research Measuring determinants of occupational health related behavior in Flemish farmers: An application of the Theory of Planned Behavior."— Presentation transcript:

1 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.pdffarmer.pdf

2 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).

3 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).

4 Introduction it 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.

5 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 approach them.

6 Introduction Reducing these practices, using protective. use can be made of behavioral models to investigate the determinants of health– and safety–related behavior among farmer

7 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

8 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

9 objectives The present study aimed to contribute to the study of the determinants of occupational health and safety– related behaviors of farmers developing 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

10 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 farmers Each farmer in the sample received a copy of the questionnaire by mail, together with an accompanying letter instructions, and a stamped envelope for return delivery

11 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.

12 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 several activities were combined

13 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

14 Instrument Machinery use: 4 items were formulated to measure attitud e, I find it important to always shield the 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 a tractor before complete standstill

15 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 the stables are ventilated properly

16 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 have both my hands free when I climb a ladder

17 Instrument pesticide 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 protective clothing 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–point Likert scale (1=strongly disagree, 2=disagree, 3=no opinion, 4=agree, 5=strongly agree). In addition, a number of items were included asking for personal and demographic characteristics and professional activity.

18 Instrument All items were phrased as statements to be rated on a 5–point Likert scale (1=strongly disagree, 2=disagree, 3=no opinion, 4=agree, 5=strongly agree). In addition, a number of items were included asking for personal and demographic characteristics and professional activity.

19 Statistical analyses These were (a) items with 95% or more of the given answers in the same category, and (b) items with a standard deviation lower than.75 The 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.

20 Statistical analyses In 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.08reasonable errors of approximation in the population GFI, AGFI, CFI >0. 90pca.docxpca.docx

21 Statistical analyses Principal Component Analyses (PCA) were performed to examine the underlying structure of the questionnairepca.docxpca.docx The internal consistency of the scales obtained through the PCA was tested by means of the Cronbach alpha To control for the length of the scales, the Spearman Brown prophecy formula was applied Pearson correlations were computed between the scales for each behavior

22 Statistical analyses multiple regression analyses were applied to evaluate whether attitude, subjective norm, and perceived behavioral control

23 Results Item 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

24 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

25 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.

26 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

27 Reliability analysis Inter–scale correlations:Table 2.farmer.pdffarmer.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

28 Multiple regression analysis MultipleMultiple regression  Table 3 Table 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 significan t 28

29 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,

30 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)

31 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).

32 Discussion A significant percentage of the variance of intentions and behaviors among farmers is explained by the components of the model Overall, the current study demonstrated the validity of the Theory of Planned Behavior in predicting behavior related to occupational safety and health among farmers, and produced a valid and reliable questionnaire to measure the cognitive concepts featured in this model

33 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 may therefore be more effective than the existing programs

34 References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179−211. Ajzen, I., & Fishbein, M. (1974). Understanding Attitudes and Predicting Social 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 of Experimental 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–control analysis of high pesticide exposure events from the Agricultural Health Study. 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 of DDTand risk of non Hodgkin's lymphoma: pooled analysis of three case control studies in the United States. Occupational and Environmental Medicine, 55(8), 522−527. Bentler, P. M (1992). On the fit of models to covariances and methodology to 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 model fit. Sociological Methods and Research, 21, 230−258. Browning, S. R., Truszczynska, H., Reed, D., & McKnight, R. H. (1998).

35 References Agricultural injuries among older Kentucky farmers: The Farm Family Health and Hazard Surveillance Study. American Journal of Industrial Medicine, 33(4), 341−353. CAISP (2003). Agricultural injuries in Canada for 1990−2000. Kingston, Ontario: Queen’s University, Canadian Agricultural Injury Surveillance Program. (URL [2006, February 20]. Colémont, A., & Van den Broucke, S. (2006). Psychological determinants of behaviors 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 [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. 36

37 References Horsburgh, S., Feyer, A.M., & Langley, J. D. (2001). Fatal work related injuries in agricultural production and services to agriculture sectors of New Zealand, Occupational and Environmental Medicine, 58(8), 489−495. Hu, L. T., &Bentler, P.M(1995). Evaluatingmodel fit. In R. H. (Ed.), Structural equation 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. American Journal of Industrial Medicine, 40(1), 32−41. Lee, B. C., Jenkins, L. S., & Westaby, J. D. (1997). Factors influencing exposure of children to major hazards on family farms. Journal of Rural Health, 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: an analysis 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 forest workers in New Zealand. Do hours of work, rest, and recovery play a role 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 Journal of Epidemiology, 145(12), 1089−

38 References 38 Petrea, R. E. (2001). The theory of planned behavior: use and application in targeting agricultural safety and health interventions. Journal of Agricultural Safety and Health, 7(1), 7−19. Pickett,W., Brison, R. J., Niezgoda, H., & Chipman, M. L. (1995). Nonfatal farm injuries in Ontario: a population based survey. Accident Analysis and 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.

39 References Sutton, S. (1998). Explaining and predicting intentions and behavior:Howwell are we doing? Journal of Applied Social Psychology, 28, 1318−1339. Thelin, A., Jansson, B., Jacobsson, B., & Strom, H. (1997). Coxarthrosis and farm work: a case referent study. American Journal of Industrial Medicine, 32(5), 497−501. U.S. Department of Labor. (2004). (URL [2004, January 29]. Wiggins, J. S. (1973). Personality and prediction. Reading: AddisonWesley. Young, S. K. (1995). Agriculture related injuries in the parkland region of Manitoba. Canadian Family Physician, 41, 1190−1197.


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