train drivers' perception of psychosocial

June, 2021. VOL.13. ISSUE NO. 2 https://hrdc.gujaratuniversity.ac.in/Publication Page | 864

TRAIN DRIVERS’ PERCEPTION OF PSYCHOSOCIAL CORRELATES OF TRAIN ACCIDENTS IN INDIAN RAILWAYS

Ms. Jonali Sah Ms. Urmi Nanda Biswas

Abstract

Train accidents are a major cause of concern. Among the several factors which hamper safe train operations, the psychosocial factor is one such risk factor that can affect both physical and mental health and organizational outcomes such as work performance and effectiveness thereby contributing to accidents. The contribution of psychosocial factors has been explored in the field of health but not much has been done in the field of train driving. The study aims to identify the psychosocial factors which hinder safe train operations by developing a scale, especially for Indian Railway train drivers. The scale was developed based on findings of the pilot study, and a review of relevant literature. Train Drivers presently working in the Indian railways formed the sample of the study. Data was collected were subjected to content analysis, descriptive statistics, factor analysis, reliability, validity and correlation analysis. The scale developed consists of 31 items, measures five psychosocial parameters; job demands, noncompliance, personal issues, work-related factors, and work engagement. These five psychosocial factors were recognized as highly contributory to accidents in both qualitative and quantitative studies.

Keywords: Noncompliance, psychosocial factors, train accidents, train drivers, Indian Railways

Introduction

Trains are an important means of public transportation in a country like India. It not only connects the length and breadth of the country but also contributes substantially to the economy of the country. Train accidents hamper and shunt development. Studies show that nearly 80% of all train accidents occur due to human error (Baysari et al.; Bhushan, Arya. & Agarwal). Train operators (drivers, signallers, and controllers) in particular have been accountable for these accidents (Kyriakidis). Human errors encompass “all those occasions in which a planned sequence of mental or physical activities fails to achieve its intended outcome, and when these failures cannot be attributed to the intervention of some chance agency” (Reason, p.9). Two types of failures (active and latent) lead to human errors. Active failures, are the direct and immediate cause of accidents (unsafe acts) and are usually made by front-line staff such as drivers or maintenance staff. Latent failures are those aspects of an organisation that influence human behaviour and make active failures more likely (Reason). Psychosocial factors can be viewed as latent conditions which give

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rise to latent failures. International Labour Organisation, define psychosocial factors as interactions between and among occupational conditions (work environment, job content, organisational conditions), and individual factors (workers' capacities, needs, culture, personal extra-job considerations) and personal characteristics (perceptions and experience) of the individual. A negative interaction between occupational conditions and individual factors and personal characteristics may affect both physical and mental health as well as organizational outcomes such as work performance and job satisfaction (International Labour Office).

Train driver’s job is very demanding as they have to maintain the safety and punctuality of trains (Kecklund et al.). Train driving is difficult as the natural train-driving environment is complex, unpredictable, and highly dynamic (Naweed). For safe train operations, effective integration of both technical and psychological expertise is required by train drivers. This expertise and abilities may be influenced by numerous environmental, job related, organizational, social, individual, psychological, and psychosocial factors, etc. The interaction among these factors imposes demands (physiological and psychological) on train drivers. When the perceived demand exceeds the train driver’s capacity to cope, it leads to stress and burnout (Carayon and Smith) that affects performance. Hence, making the system vulnerable to operational hazards and failures (Wiegmann and Shappell; Mazloumi et al.).

The psychosocial domains studied by occupational health researchers include work environment, job demands, job control (decision latitude), social support, workload, work schedule, organizational culture, interpersonal and intrinsic and extrinsic rewards (Siegrist; Larsman; Leka and Jain). Work-related absence due to health outcomes, workplace injury, have been associated with workplace psychosocial factors (see, Lu et al.). Job dissatisfaction, work-related stress, absenteeism, poor health and wellbeing, workplace injury, Lack of autonomy, high job demands, inadequate social relations, conflicting tasks, conflict of values, constraints posed by having to comply with rules and regulations, have been associated with workplace poor psychosocial factors work environment (Eurofound and EU-OSHA; see, Lu et al.).

Train drivers are exposed to various types of job-related demands, which include solitary work, limited opportunities for social contact, irregular working hours, co-workers’ aggression, conflicting tasks, responsibility for operating the train by maintaining both safety and punctuality, and work pressure (Kecklund et al.) that can impact the work performances.

The operation of the Indian Railways is changing in response to a range of commercial, political and social influences. With the advent of new technologies and an increase in the number of trains, train drivers are facing a lot of challenges in train operations. There have been very few researches, on psychosocial factors concerning train drivers particularly in terms of safety (Kecklund et al.) and still fewer in the Indian Railways. Train drivers are the most important people directly related to train operations. Identification of psychosocial factors which affect train drivers would be helpful to prevent accidents (hazards) and improve work efficiency. The present research attempts to develop a measure for identifying the psychosocial factors which are perceived as responsible for train accidents by train drivers and use them as predictors of their accident proneness in the Indian Railways.



Psychosocial factors here are those organizational, psychological, and social aspects of work factors that have the potential for influencing Train Driver’s work. The objective of the paper is to first understand the train drivers about their perception of these accident responsible factors and then to develop a standardised scale for assessment of the vulnerabilities of drivers for accidents by use of this scale.

Methodology The study comprised of two sections a) In-depth interview of loco pilots which served as the

pilot study b) development of an assessment tool to measure the perceived psychosocial factors responsible for train accidents. The development of the tool was based on the findings of the interviews.

a) Pilot study Eight train drivers, with a minimum of 2 years of experience were interviewed with the help

of a structured interview guideline. The interview explored the problems faced and the causes of train accidents, and SPAD (Signal Passed at Danger) as perceived by train drivers. Data generated during the interview was content analysed. Using thematic analysis, six broad themes were identified as follows: (i) environmental (cab design), (ii) social (work-family interference), (iii) job/work-related (job demands, job dissatisfaction, irregular work schedule, workload,), (iv) personal/individual (motivational, circadian rhythm), (v) organizational (training and refresher course, punishments, culture) and (vi) noncompliance (violation of rules and regulation) having twelve sub-parameters. The number of times an obstacle was mentioned by each participant was noted to examine patterns and draw conclusions. Some of the major issues identified are:

the cabin of the engine where the driver's station themselves, is very hot and noisy and hence was considered a hindrance to smooth functioning.

being away from family for a long-duration, due to irregular work schedules, and inappropriate facilities for travel from work to home created family problems.

High work responsibility, low control over the work schedule, lack of autonomy, inflexible time table, unpredictable working hours, requirement of a high level of concentration and alertness took a toll on them.

Running room (resting place) facilities not appropriate, inadequate resting facility. Railway quarter facilities not good.

Not enough leaves. Leaves are not approved easily.

Very long working hours, too much workload, high level of stress

No system for taking feedback or suggestions etc. These issues were discussed with competent authorities like train driver’s supervisors from

Indian Railways, and their insights were further modified for the development of questionnaires. b) Development of the measure

The researchers conceptualized the parameters for the assessment tool based on the issues and parameters found from the pilot study and with reference to existing questionnaires. Items were adapted from Ryan et al., and Mazloumi et al., in some sections of the questionnaire. These items were modified to make them suitable for Indian Railway train drivers. The questionnaire developed contained 75 items; 20 items measuring demographic variables, and 55 items to identify



psychosocial factors that may contribute to accidents. All items were measured on 5 points Likert type scale where higher scores meant a higher magnitude of the contribution of that item in accident proneness. The items were coded as 1-Not contributory, 2-Lesscontributory, 3-Moderately contributory, 4-More contributory, and 5-Most contributory. There were no reverse-scored items. The questionnaire was originally prepared in English. A Hindi version was prepared by using translation and back translation for establishing translingual equivalence. Sample

The sample for the study were 914 male train drivers with a minimum of 2 years of experience in the Indian railways. Sample breakup according to different demographic variables/categories are as follows: The majority of the sample belonged to the age group of under 34 years (50.2%), followed by 35-44 years (25.1%), and 45 above (24.7%). Educational qualification of the Train Drivers was 10+ITI (Industrial Training Institute, 61.4%), and Bachelors of Technology (B. Tech and others, 38.6%). Four types of train drivers took part in the study, Mail/Express Loco Pilots (12.4%), Mail/Express Assistant Loco Pilots and (24.1%), Goods Loco Pilots (28.2%), and Goods Assistant Loco Pilots, (35.3%).

The questionnaire was administered to train drivers from Ratlam and Vadodara divisions (Western Railway zone) of the Indian Railways. Data were collected personally from train drivers at their Training institutes after taking prior permission from the concerned authorities in one and a half years. On average, it took 40 to 45 minutes to complete the survey. The response rate of the questionnaire was 98%.

Results

After cleaning the data for wild codes, and missing values, items with a median value of 3 and above were selected, as those items indicated the highest ratings (above 55%; when the rating of moderately, more, and most contributory were combined). These items were perceived as most contributory to accidents by train drivers. Selection resulted in 31 items, which were then subjected to exploratory factor analysis.

Principal Component Factor Analysis (PCA): PCA recognizes duplication between items in different sections, reduces the number of items into fewer components/factors and explores the presence of sub-themes items that measure the underlying constructs. PCA was carried out using varimax rotation. Factors with an Eigenvalue of 1 were chosen. Five factors were extracted through 9 iterations explaining a total of (52.443%) variance in the data. The factor loadings of items varied from .48 to .75. The factors were examined and named appropriately. In factor 1, thirteen items loaded, explaining 17.00%, variance; six items loaded on factor 2, explaining 10.31% variance; four items loaded on third factor 3, explaining 8.80% variance; three items, explaining 8.23% variance, loaded on factor 4, while five items explaining 8.11% variance loaded on factor 5.

Four items (Difficulty in leave approval, Frequent change in duty hours, Frequent change in route-wise working schedule, and Inattentiveness at work), cross-loaded on more than one factor; the difference in loading on the factors were less than 0.2 (see Ryan et al., 2008), and hence were excluded from the questionnaire. One item (Health problems), was also omitted as its factor loading was less than .3, it also had the lowest communality which clearly shows that it does not share much variance with other items.



Table1.

Results from Factor Analysis and Reliability Analysis of psychosocial items (N=914)

Sr. No.

Psychosocial factors

Items Factor loadings

Cronbach α

1. Job demands B39-Shortage of required staff at the workplace.

0.705 0.887

B42-The physical working conditions / infrastructural facilities at the Loco.

0.672

B40-Social isolation due to the nature of the job.

0.672

B28-Mental pressure due to the nature of work.

0.655

B51-Work overtime/working beyond the routine hours at the workplace.

0.638

B36-Physical conditions and facilities at the running rooms.

0.623

B33-Organisation of shifts. 0.6

B45-Too much of the job responsibilities given.

0.598

B37-Pressure to maintain the punctuality of the trains.

0.581

B18-The nature of work schedule and timings are such that they interfere and come across personal lives and leisure activities.

0.557

B31-Not getting enough call time (duty preparation time) for the next spell of duty.

0.548

B25-Lack of proper rest. 0.526

B17-Intense concentration required for long hours.

0.524

2. Noncompliance B34-Over speeding of the train. 0.756 0.818

B41-Taking drugs/alcohol. 0.736

B35-Overconfidence (excessive motivation).

0.711

B49-Use of mobile phones and other gadgets while driving.

0.662

B38-Risk taking attitude. 0.575

B30-Non-compliance with rules and regulations at work.

0.56



3. Personal issues B1-Aggressive behaviour/ aggression. 0.649 0.751

B2-Anxiety/ Nervousness. 0.622

B3-Being away from family for a long duration.

0.62

B9-Family problems 0.587

4. Work-related factors

B5-Confusions/misunderstandings at your workplace.

0.763 0.846

B4-Casual attitude towards work. 0.725

B8-Distractions at the workplace. 0.687

5. Work engagement

B20-Lack of confidence. 0.666 0.766

B21-Lack of proper experience. 0.612

B15-Inability to process and use the information gained at times of need.

0.562

B27-Lower level of commitment at work 0.532

B12-Having a lack of power and influence in taking important decisions.

0.487

Factor naming and operationalization of parameters in the assessment tool of psychosocial

factors

Factor 1: Job demand - It measures the extent of demand the job makes on the person. Broadly there are two types of demands, 1) physical demand, in terms of workload, and psychological demand (cognitive or emotional). Its outcome depends on the stress sources present in the environment as well as the individual’s ability to cope with it. Those who perceive greater demand in the job have a higher propensity towards accident proneness.

Factor 2: Noncompliance - Personal characteristics determine that those who do not comply with rules and regulations are more prone to accidents.

Factor 3: Personal issues – The dimension captures the individual and family-related problems that have some importance in the occurrence of accidents. A high score in the personal issues indicates the degree of personal issues interfering with work.

Factor 4: Work-related factors - It captures the contextual variable of the work environment. A high score in this factor will indicate a negative perception of the work environment and hence more prone to accidents.

Factor 5: Work engagement –Work engagement is making use of oneself to the work roles in the organisation. How work is perceived leads to either burnout (leading to decreased productivity) or an increase in motivation (leading to increased productivity). A higher score shows less engagement and a low score shows more engagement.



Following the standardization protocol, the developed measure with the five factors was assessed for the reliability and validity Reliability

The reliability of the scale was studied by finding the internal consistency within the factors using Cronbach’s alpha (α). Three factors Job demands, Noncompliance and Work-related factors showed very high internal consistency with an overall α greater than 0.8. While Personal issues and Work engagement showed Cronbach’s alpha above 0.76. Validity

The face validity and content validity of the questionnaire were checked with the help of experts working in senior positions with Indian Railways and psychology professors. Criterion validity was analyzed by choosing stress as a criterion. Perception of high distress at work situation (through the psychosocial factors in job context and work environment) should show a positive correlation with the perception of stress. ‘A Global Measure of Perceived Stress Scale’ developed by Sheldon Cohen, Tom Kamarck, and Robin Mermelstein, (1983) was used for this. It measures the degree to which situations in one’s life are appraised as stressful (Cohen et al.). Pearson’s product-moment correlation was carried out to measure the criterion validity of the prepared questionnaire. Tables 2. Descriptive Statistics and Correlation coefficient for Stress and Psychosocial factors (N =914) Variables Mean SD 1 2 3 4 5 6 1 Total Stress 19.52 5.92 -

2 Job Demands 41.23 10.56 .31** -

3 Noncompliance 21.82 5.86 .02 .32** -

4 Personal issues 11.93 3.79 .27** .55** .31** -

5 Work-related factors 11.19 3.32 -.03 .34** .52** .48** -

6 Work engagement 13.62 4.33 .16** .48** .59** .46** .56** - Note. N=total sample size, Table 2, shows job demands, personal issues and work engagement were positively correlated with stress. Stress has a moderate correlation with job demands. However; stress and personal issues and work engagement have a low correlation with each other. No correlation was observed between non-compliance factors, work-related factors and stress.

Discussion

The research aimed to develop a standardised measure to assess the psychosocial factors which might be working as precursors for accident-prone behaviour among train drivers. Through the exhaustive qualitative interview, expert validation of interview findings, and development of the measure following the standard procedure of scale development, the researchers finalized the scale as “psychosocial precursors to train accidents”, which is an assessment tool to assess train drivers’ vulnerabilities towards accident and Signal Passed at Danger (SPAD). The scale consists of 31 items with five parameters (job demands, noncompliance, personal issues, work-related factors, and work engagement factors). These five psychosocial factors were recognized as highly contributory to accidents in both qualitative and quantitative studies. Poor work environment, high job demand, low job control affects health adversely (Siegrist and Marmot) thereby increasing the risk of being injured in an occupational accident (Swaen et

Towards Excellence: An Indexed, Refereed & Peer Reviewed Journal of Higher Education / Ms. Jonali sah & Ms. Urmi Biswas/64-873


al.). Organisational, emotional, and personal factors together with social relations of people have been identified as determinants of accidents proneness (Kirschenbaum et al.). Personal, dynamic personal, task, team, organizational, system, and environmental factors have been identified as railway performance shaping factors (R-PSF) (Kyriakidis). Environmental, task-related, social, individual, and organizational factors have been associated with performance obstacles of train drivers in Iran (Mazloumi et al.). Cox and Griffiths, (2005) and ILO (1986), have identified similar psychosocial hazards dangerous to the physical and psychological wellbeing of the employee at the workplace have been identified (International Labour Organisation). Stress at work is associated with heart disease, depression, and musculoskeletal disorders and high job demands, low control, and effort-reward imbalance are risk factors for mental and physical health problems (Leka and Jain). These psychosocial factors, by inflicting the risk of injuries at work influence work performance. Hence, the findings of the present study are well supported by previous findings.

Implications

The developed scale, “psychosocial precursors to train accidents” has significant implications to identify the train drivers who are vulnerable to commit human errors in driving. Once identified, (i) the psychosocial precursors can be eliminated thereby reducing their negative impact on train drivers (ii) these drivers can be given training to improve their perception, attitude and behaviour, and (iii) customised counselling and group counselling modules can be developed to help train drivers reduce their level of stress due to work. All these interventions can help improve and facilitate the optimal safe functioning of the train drivers.



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Ms. Jonali Sah Department of Psychology

The M. S. University of Baroda, Vadodara [email protected]

& Ms. Urmi Nanda Biswas

The M. S. University of Baroda Vadodara

train drivers' perception of psychosocial

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