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

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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).

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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).

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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.

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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.

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IntroductionReducing these practices, using protective.

use can be made of behavioral models to investigate the determinants of health– and safety–related behavior among farmer

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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

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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

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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

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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

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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.

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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

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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

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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

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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

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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

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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.

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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.

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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.

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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

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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

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

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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

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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

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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.

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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

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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

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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,

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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)

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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).

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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

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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

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