Textbook of Maritime Medicine

There are two main types of research. Physicians mainly use quantitative research methods. Researchers that are influenced by the humanistic approach, e.g. anthropologists use qualitative research methods that deal with factors such as values, concerns and behavior. This way of research, however, is not unfamiliar to physicians [3] and may also cover case studies. The two ways of conducting research may often contribute significantly to defining and understanding a problem and to solving it, and at times it may be necessary to combine them.

18.4.1   Quantitative research methods in maritime medicine – maritime epidemiology

Observational epidemiological studies may be descriptive or analytical. The descriptive epidemiology describes the disease pattern in a well-defined population while the analytical epidemiology relates the disease pattern to factors such as exposures, life styles, treatments or vulnerability.

In some cases it may still be relevant to study incident descriptive data even if the source population is unknown. A multinational surveillance study of accident reporting in a number of countries has provided valuable results in spite of this constraint [4].

An analytical approach may relate the morbidity in an exposed population to the morbidity in a non-exposed population selected in a way so that the non-exposed population would have the same morbidity, if the studied exposure neither prevents nor causes the studied disease. In quantitative research it is mostly the aim to compare health outcomes in one group characterized in a certain way, e.g. presence of a disease, an exposure or a treatment, with the outcomes among controls without that feature. In the clinical controlled trial, one has to select controls out of those with the disorder for which a treatment is to be studied. The subjects are randomly allocated to one or the other group. This design is typically chosen in the study of the effect of a certain medication. A similar design may be feasible with an intervention directed towards preventing the effect of a certain exposure, such as e.g. seasickness. Seafarers in risk are allocated at random to the intervention. In contrast, if new clinical or experimental variables are to be measured in a risk group (e.g. nosographical or pathogenetic factors such as obesity or metabolic syndrome among seafarers, or subjects exposed to a certain potentially harmful factor), the controls cannot belong to that risk group but should be taken from the general population. In this case one would aim to make the two groups that are to be compared as much alike as possible except for the studied factor by matching them with regard to e.g. sex, age or social status.

For diagnostic assessment, it is paramount that the applied tests (physical examinations, laboratory studies, questionnaire information etc. are able to identify the subjects with the studied condition (sensitivity) but not those without the condition (specificity). Based on these data the predictive (positive and negative) ability of the test can be calculated. It is, however, important to realize that no test is perfect and that many commonly applied tests, e.g. many tests in the physical examinations, are really not very good for the purpose for which they are applied. It is also important to realize that predictive values cannot be uncritically transferred from one setting to another (see spectrum bias below).

Exposure assessment has also many challenges. Exposure assessment is the process of estimating or measuring the magnitude, frequency and duration of exposure to an agent or a physical or psychosocial factor, along with the number and characteristics of the population exposed. Ideally, it should describe the sources, pathways, routes, and the uncertainties that may occur in the assessment.

Design of epidemiological studies

Based on the data collected by the researcher that are to be compared, three main designs of observational studies are particularly relevant in occupational and maritime medicine. Each of them has its strengths and limitations.

• The cross-sectional study, in which the exposure data and effect data are registered at the time of investigation, i.e. data of prevalence character. The disease prevalence with and without exposures is then compared. The cross-sectional studies are mainly of a descriptive type by unfolding correlations but without indicating the direction of a relationship. They describe the disease prevalence, which represents the incidence as well as the duration and therefore cannot distinguish between etiologic and prognostic factors. The cross-sectional study is a simple and comparably rapid approach. Cross-sectional studies form a class of research methods that involve observation of all of a population, or a representative subset, at a defined time. They differ from case-control studies in that they aim to provide data on the entire population under study, whereas case-control studies typically include only individuals with a specific characteristic, with a sample, often a tiny minority, of the rest of the population. Both are a type of observational study. Unlike case-control studies, cross-sectional studies can be used to describe absolute risks and not only relative risks. They may be used to describe some feature of the population, such as prevalence of an illness, or they may support inferences of cause and effect. Longitudinal studies  (se below) differ from both in making a series of observations more than once on members of the study population over a period of time.
• The cohort study (prospective study, follow-up study) is a longitudinal study that permits the identification of changes in, e.g. the incidence of a certain disease, over time and the estimation of the relative and absolute influence of an exposure on the disease. In the cohort study, a population in which some are exposed and others are not is followed-up with regard to disease outcome. The incidence with and without exposure is then compared. These studies are easy to understand and design but may be difficult and costly in practice. The two groups should be comparable with regard to everything but the studied exposure and should usually be followed-up during a long observation time. For rare outcomes, the cohort studies are not suitable because a sufficient number of cases for analysis would demand a very large sample. The relative risk and risk difference is expressed by the quotient of and difference between the cumulative disease incidence in the exposed vs. the non-exposed subjects, respectively. The establishment of a maritime cohort has been demonstrated by e.g. Strand et al. [5] and Hansen et al. [6].
• The case-control study represents an approach, which has the advantage of being relatively cheap and rapid because it does not require a follow up time. It is also useful for the study of conditions that are infrequent because it is based on a design in which all cases (all with the disease one wants to study) are compared with a representative sample of the population from which the subjects with the disorder derive that do not have the studied disorder. The odds-ratio is the quotient of the proportion of the diseased to the healthy subjects in the exposed and non-exposed groups, respectively. This design should not be used if the studied exposure status is rare or if there is a major drop out of control subjects. The odds-ratio may be close to the relative risk for rare disorders, but it does not represent the relative risk and is not a true measure of association.

Data collection and the importance of choosing the right measures and tests and to perform them with accuracy

Data collection describes the process of preparing and collecting data as part of a research project and aims to obtain information regarding a specific topic to keep on record, to make decisions about important issues, and to pass information on to others. Data collection usually takes place early in a project, and may be formalized through a data collection plan containing agreement with regard to goals, target data, definitions and methods. Following the data collection some form of sorting, analysis and/or presentation will take place.

The pre-collection activity is a crucial step in the process. It is often discovered too late that the value of the collected data is discounted due to poor sampling. After the pre-collection activity is fully completed, data collection in the field can be carried out in a structured, systematic and scientific way.

A formal data collection process will ensure that data gathered is both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid. The process provides both a baseline from which to measure from and in certain cases a target on what to improve.

The collected data may be of a biological character, e.g. biochemical or physiological, or may represent exposures. Both can be measured or semi-quantified. Of course it is crucial that the applied measures are reproducible and valid, i.e. represent the truth. Questionnaires are a frequent source of data for which the same demands can be applied as for any other means of assessing disease, symptoms, level of functioning, well-being, exposures etc.: The collected data should be valid and relevant to the studied issue. One special limitation for questionnaire data that should be mentioned here is that one does only get answers to the raised questions. Consequently, the data that are to be collected by use of questionnaire should be scrutinized previous to the launch of the questionnaire. In most cases, a pilot study will indicate if the questionnaire will provide the data you need.

The main types of data collection include biological data from laboratory or physical examinations, questionnaires, interviews, census, sample surveys, and administrative by-products. Each of these data sources has their respective advantages and disadvantages. A census refers to data collection about everyone or everything in a group or population and has advantages, such as accuracy and detail and disadvantages, such as cost and time. A sample survey is a data collection method that includes only part of the total population and has advantages, such as cost and time and disadvantages, such as accuracy and detail. Administrative by-product data is collected as a byproduct of an organization’s daily operations and has advantages, such as accuracy, time simplicity and disadvantages, such as lack of flexibility and control.

The direct approach measures the exposures by monitoring, e.g. the pollutant concentrations reaching the respondents. The concentrations are directly monitored on or within the person through point of contact, biological monitoring, or biomarkers. The point of contact approach indicates the total concentration reaching the host, while biological monitoring and the use of biomarkers infer the dosage of the pollutant through determination of the body burden. The respondents often record their daily activities and locations during the measurement of the pollutants to identify the potential sources, microenvironments, or human activities contributing the pollutant exposure. An advantage of the direct approach is that the exposures through multiple media (air, soil, water, food, etc.) are accounted for through one study technique. The disadvantages include the invasive nature of the data collection and associated costs.

The indirect approach measures the exposure in various locations or during specific human activities to predict the exposure distributions within a population. It focuses on the exposure within microenvironments or activities rather than the exposure directly reaching the respondents. The measured data are correlated to large-scale activity pattern data, to determine the predicted exposure. This exposure modeling determines the estimated exposure distributions within a population rather than the direct exposure an individual has experienced. The advantage is that the process is minimally invasive to the population and is associated with lower costs than the direct approach. A disadvantage is that the results were determined independently of any actual exposures, so the exposure distribution is open to errors from any inaccuracies in the assumptions made during the study, the time-activity data, or the measured exposure data.

In general, direct methods tend to be more accurate but more costly in terms of resources and demands placed on the subject being measured. For both approaches it is crucial that measurements are accurate.

Register-based studies

Public and private registers may provide valuable information that can be used for setting priorities, identifying risk exposures, designing and implementing interventions and assessing their effect over time. These registers include national registers of seafarers, hospital admission registers, registers of notified accidents and work-related disorders, registries of other notified conditions, e.g. infectious diseases or cancer, insurance based registers, and union member files.

Analyses of maritime register data do not in their substance differ from the above descriptions of epidemiological studies but are characterized by a number of challenges. Similar to what is required for making valid risk assessments and comparisons in any epidemiological analysis, register-based studies demand that both the denominator (e.g. the number of risk persons/exposed persons/seafarers) and the numerator (e.g. the number of persons that suffered a disease, an accident) should be known or at least be estimated with some certainty from the available data.

There are two main problems with regard to the denominator:

• Due to the casual nature of many jobs at sea epidemiological studies are methodologically difficult to undertake because the denominator would be unstable. This may be a major problem because workers that are employed on casual terms may be particularly at risk due to the pressures created by the lack of a stable career.
• Another challenge is the difficulties with regard to characterizing the denominator. This is due to the fact that public and other registers may vary from country to country, over time and from setting to setting. For this reason, external, e.g. international, comparisons are often difficult to undertake and may even be impossible, in particular if the details of the composition of the registry is not described. At times even a national register may change considerable over time.

The challenges with regard to the numerator are no less.

• Registers may be (and mostly are) incomplete, and usually information need to be found from multiple sources.
• Large differences between registers complicate or preclude comparisons. For example offshore accidents are notified to the authorities after one day off work in some countries and after three days in others.
• The identification of a case may be difficult or inaccurate. E.g., the diagnostic distribution of upper limb disorders in a register of notified work-related diseases is likely to be inaccurate since there is little consensus of the diagnostic criteria to be applied and because the majority of work-related conditions are currently uncovered by diagnostic criteria.

A publication about maritime work-related mortality illustrates all these difficulties [6] and how they are sought to be overcome by meticulous studies of multiple databases, and files from various sources. In this case the effect measure (death) is rather robust although occasionally the assessment of the mode of death did cause difficulties. If, however, the outcome was a disease many additional constraints may arise, e.g. with respect to the diagnostic criteria as mentioned previously, or to the setting in which the sampling was done. When analyzing hospital based registers, one should be aware of the fact that there is a threshold for referring to hospital and that this threshold is depending of the diagnosis, the severity of the studied disorders, and various diagnostic practices in between hospitals and countries [7, 8].

Other complicating factors when register based data are compared include the various ways in which the data are provided, e.g. as the number of persons with disease pr. 1.000 or 10.000 persons. In an occupational context this may be particularly misleading because the persons also represent observation time. Therefore, comparisons between maritime occupations and other industries are invalid. One example could be comparison of the work-related mortality of fishermen to that of workers ashore. The risk may change over time and also varies in between and over time for the individual fishermen, who do not all work the same schedule. The best way to overcome this problem is to convert the figures into, e.g. the number of deaths pr. 1.000.000 working hours.

Statistics

Descriptive statistics are used to describe the main features of a collection of data in quantitative terms. For describing situations, the statistical summary measures include, e.g. incidence vs. prevalence vs. cumulative incidence, mortality rate, which may be age-standardized, or years of potential life or working ability lost.

Descriptive statistics are distinguished from inferential or inductive statistics in that descriptive statistics aim to quantitatively summarize a data set, rather than being used to support inferential statements about the population that the data are thought to represent. Even when a data analysis draws its main conclusions using inductive statistical analysis, descriptive statistics are generally presented along with more formal analyses. For example a paper reporting on a study involving human subjects will typically contain a table giving the overall sample size, sample sizes in important subgroups (e.g. for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of males and females, and the proportion of subjects with related co-morbidities.

In research involving comparisons between groups (analytical epidemiology), a major emphasis is often placed on the significance level for the hypothesis that the groups being compared differ to a greater degree than would be expected by chance. This significance level is often represented as a p-value, or sometimes as the standard score of a test statistic. In contrast, an effect size is a descriptive statistic that conveys the estimated magnitude and direction of the difference between groups, without regard to whether the difference is statistically significant. Reporting significance levels without effect sizes is often criticized, since for large sample sizes even small effects of little practical importance can be highly statistically significant. Effect sizes such as the absolute risk reduction, the number needed to treat (or harm), the odds ratio or relative risk are better to demonstrate the severity of the problem or the ability of an intervention to reduce the risk.

The statistical calculations will not be reviewed here but the reader is referred to textbooks on medical statistics [1, 2] or web resources such as http://en.wikipedia.org/wiki/Medical_statistics.

Interventional research in maritime medicine

Interventional research is experimental of nature. In spite of this, many factors that may influence the outcome may be (and mostly are) out of the influence of the researcher. In theory, all potential risk factors are kept at a stable level except the one studied.

These ideal circumstances can usually not be reached in the practical situation in which the composition of the study group or the work conditions may change over time. Downsizing of the maritime population may have occurred due to reduced manning or economical constraints of the trade. The fishing options may have been altered by weather conditions. The route or the nature of the transported goods has changed. New regulations have induced new standards with regard to occupational health and safety and they may have counteracted existing ones.

Self-assessment of personal risk factors and the ability to take action to address these have been shown to be effective ways to shift control to or improve the control of the individual, and empower seafarers to better help themselves. This approach requires an employer-attitude that recognizes the nature of health risks, sees seafarers as an asset and not as a cost, accepts their involvement, and is prepared to invest in this view.

Such participatory approach leaves an ownership with the involved seafarers or fishermen because they themselves have designed the intervention with the assistance from researchers that can provide them material and advice, validated methods for intervention, and – in the end – demonstrate whether the intervention has provided benefit to the studied maritime population. On example of this experimental design is a comparison between the influence on mental workload and performance of various navigation methods in simulators [15].

Clinical research

Clinical research is a branch of medical science that determines the safety and effectiveness of medications, devices, diagnostic products and treatment regimens intended for human use. These may be used for prevention, treatment, diagnosis or for relieving symptoms of a disease and are also applicable in maritime medicine, e.g. in a trial of the efficacy of transdermal scopolamine for seasickness [16].

Strengths and weaknesses of various epidemiological designs

Each epidemiological design has strengths and weaknesses. The strengths relate to the relative feasibility in various settings and the timeframe and available resources. The weaknesses by the relative influence of factors such as confounding and bias that may disturb or even destroy results if not taken into account (Table 19.4.1).

Table 19.4.1. Advantages and disadvantages with various epidemiological study designs

Confounding and bias

Confounding represents the phenomenon that health effects may have multiple determinants. E.g., it has been shown that the incidence of lung cancer is increased in the maritime environments and that this in particular is the case for the machine crew [9]. The challenge could be to study whether exposure to carcinogens in the maritime setting, e.g. in the engine room, may explain this finding or whether a higher prevalence of smoking may influence the health of the maritime population and in particular that of the machine crew. If this is the case, studies of the relation of lung cancer in machine crews to the exposure in the engine room should control for the excess smoking among machinists. The concept requires that the confounder should be statistically associated with the exposure (that machinists smoke more than the comparison population), that it is an independent cause of the disease (smoking causes lung cancer), and in addition that the confounder does not constitute a link between exposure and disease. One could also study the role of smoking (or the exposure to passive smoking) of the morbidity on board ships. In this case, smoking could be regarded as an occupational risk secondary to, e.g. cultural factors or access to cheap cigarettes in the maritime setting. With this research question, exposures to carcinogenic substances deriving from the engine room or from the cargo would be confounding factors to be considered.

Bias may occur following that the information on which the study is based is wrong (bias of information), or that the results of the study are distorted due to the composition of the participants.

• Information bias may come from an inaccurate assessment of the exposure or the effect both of which are frequent sources of erroneous conclusions. E.g. in a study of upper limb disorders among fishermen, the physical examination may be able to indicate only a certain limited number of disorders among which, however, the most prevalent disorders are not included. Another frequent example is the tendency of those with a certain disease to remember exposures that are relevant to that disease while healthy colleagues are less likely to report relevant exposures.
• Bias of selection may occur if the studied persons do not represent the target group, e.g. if the motivation for participation is dependent of disease or exposure status. E.g. the response rate to a questionnaire on accidents may differ in between various groups of seafarers with those who have experienced an accident being more (or less) inclined to answer. Another example may be that noise exposed seafarers would be more inclined to participate in a study on hearing impairment than non-exposed seafarers. The “healthy worker effect” is a frequent source of bias in maritime medicine caused by seafarers with disease being likely to leave the trade. Consequently, the study of an active maritime population may result in finding less disease and an underestimate of risk.
• Spectrum bias may occur when the sensitivity and specificity differs between sub-groups of patients with various disease prevalence. Diagnostic test performances may be artificially overestimated in a case-control design where a healthy population is compared with a population with advanced disease ('sickest of the sick'). If properly analyzed, recognition of heterogeneity of subgroups can lead to insights about the test's performance in varying populations.
• Bias due to factors related to the researcher may occur from personal desires or views, from presuppositions that are wrong, or from selecting arguments in the discussion that favors certain viewpoints and tend to neglect others. Presuppositions may be based on e.g. political viewpoints or selective sympathies and antipathies. Well-intended desires to benefit an exposed group of workers or to protect the employer from expenses are other examples of presuppositions that may lead to bias when these actions are not justified from the research.
• Publication bias may also be personal if the researcher prefers to come out with a positive finding in contrast to a negative one, but may also relate to the willingness of journals to publish positive results rather than negative ones. 

Differentiated misclassification of information or participation occurs when there is a flawed measure of, e.g. disease among the exposed that differs from that of the non-exposed. Differentiated misclassification may cause bias in both directions, and the effect of exposure may be consequently over- or underestimated. In contrast, non-differentiated misclassification in which the measures are equally flawed in the two groups tends to mainly lead to bias towards the 0-hypothesis meaning no or minor association between exposure and effect.

An effective measure that can address some of the aspects of bias is to design the study in such a way that the study person (e.g. patient and control) and the researcher does not know to which group the examined person belongs. Single blinding refers to ignorance of the examiner with regard to status. Double blinding demands that the study subjects do also not know their status. With blinding, the risk of bias due to pre-existing views or expectations from the researcher as well as the study person can be reduced. Blinding may be rather easy in a randomized controlled trial in which some subjects are given the potentially active medication and others the non-active medication (placebo). Neither the researcher nor the study person knows to which group each study person belongs (double blind study). In a diagnostic trial, one must frequently merely accept blinding of the examiner (single blind study) because the study subjects are often aware of their status as with or without the disease. A similar limitation may occur in, e.g. cohort studies, if the study subjects know their disease status at follow-up. In occupational and maritime intervention studies, blinding of the examiner as well as the study persons is often impossible because both are likely to note who are subjected to the intervention and who serve as controls. A placebo intervention is mostly not feasible.

19.4.2  Qualitative research methods in maritime medicine

“Qualitative methods” in research cover a wide range of scientific fields and underlying theoretical aspects. They are applied in medicine and social sciences as well as in arts and humanities, but in different manners. What is common for all these applications is the qualitative perspective. Rather than determining, e.g. causal inference, qualitative research methods can approach such a research question from multiple angles and thereby provide significant arguments for determining the interventions that are likely to be helpful. This is in contrast to quantitative research, which analyzes the correlation between various data, i.e. between exposures and effects, but without being neither always able to determine the causal relation nor to contribute to the understanding of such relations.

This brief review of qualitative research intends to promote qualitative research hoping that the reader will achieve some understanding of this type of research with its own methods and paradigms and to understand that qualitative research can contribute to the common ambitions in terms of improving the health, safety and wellbeing of seafarers.

Qualitative research differs from other research in the interplay between theory and method, which is usually evident in quantitative research. It should be recognized that it should not try to imitate the requirements of natural science for universal truths and methodological rigor. Qualitative research techniques may be more an interaction between theory and empirical data. The four tools represented by methodology, concepts, theories, and theoretical perspective cannot be separated and their interaction is the focus in a qualitative perspective.

Qualitative research therefore aims to constitute a relational, holistic and contextual approach. The interaction between people is the key focus in qualitative research with identity being regarded as a relational and not an essential factor. The holistic approach means that the local relates to the global. The implication of that is that even though a particular dimension deals with e.g. culture, this is not considered in isolation but in conjunction with other dimensions. The concept of "embeddedness" is often applied and has proved useful for studies in the maritime setting: Culture is embedded in social, political, economic, etc factors. This contextual approach means that empiricism often consists of the daily life, e.g. on board a ship, and that the fieldwork takes place at the micro level. Fieldwork requires drastic (thematic or geographical) boundaries, but the empirical data generated from fieldwork must be contextualized and (re)placed in a larger context.

Consequent to this approach, qualitative research can neither be a neutral science free of values nor can it represent a definitive truth. There is no neutral position from which the world can be viewed. Regardless of status as a researcher or a researched subject, the 'reality' is always viewed from a given position and may be potentially disturbed by a set of biases or prejudices. The point is to include our positioning, our bias or our assumptions as parts of the tool for the analysis. Only by positioning, one can access the universe of others.

Especially with regard to applied research, one may - in a context in which power is never neutral – be caught in various players 'truths' and perspectives. The role here is to embrace more context than the individual player is able to hold and then subsequently reflect on the achieved results with both players and academic colleagues.

Consequently, the research outcomes always represent provisional truths meaning that may still be under debate. While the statements or observations can sometimes be judged as either true or false, it may well be more interesting to address the question of how and why the various statements are produced. The question will determine the answer you get but one of the forces of qualitative research is that you are also likely to get answers to questions you have not asked. This is a key reason that one cannot apply the same criteria and demand the same rigor as in quantitative research.

The prime methods in qualitative research consist of interviews, focus groups, participant observations and case studies. Participant observation is obvious for the purpose, but an interview may also provide new answers, if following the respondent is emphasized rather than following the interview guide. The interviewee takes the lead with the support of the interviewer. This is an advantage on several levels: The interview promotes exchange, positioning and balance between guiding and laissez-faire, and at the same time, the interviewed person is acknowledged as an active, creative and interpretive co-worker. Getting unexpected answers is more likely when the interaction is open to all potentially data-producing situations. It is important to save strange, inexplicable or contradictory observations or statements. They may eventually turn out to be extremely important keys to a more complex level of analysis.

Standardization of qualitative methods is neither possible nor desirable. This does not mean that everything is permitted. In contrast, guidelines for good science are constantly refined (Table 19.4.2)

Table 19.4.2   Quality in qualitative research: Enabling and impending conditions [10]

The qualitative researcher should be conscious and explicit about ones prospects and articulate the basis for the interpretation in terms of the empirical data and assumptions, e.g. the preconceptions from own personal experiences of relevance that may have influenced the theoretical approach. They are obviously difficult to define, and the researcher is not always conscious about them while the theoretical approach is the least problematic. The reflexivity is a part of the social context that the researcher describes. Consequently, ones position may not be limited to that of a researcher but may also relate to ones gender, age, nationality etc.

The researcher would always aim for an internal logic and consistency. The empirical anchorage can be strengthened through triangulation such as viewing the question from various angles and buy using multiple data or methods, but the triangulation cannot verify convergence (consensus). On the contrary, differences may serve as important means of detecting contradictions such as when two parties have two different versions of an event. The same apply for paradoxes. A major strength of qualitative research is that it can take account of and deal with contradictions and paradoxes.

A key issue is whether the players accept the outcome of an analysis. Their validation could indicate empirical anchorage, but may also lead to an unwanted introspection. The conflict of interests may be so large that the full approval by one sort of players indicates an evidence of imbalance!  The researcher is aiming for internal consistency meaning that "each piece must fit into the puzzle". However, the pieces that do not match may well be the most interesting ones. It should be preferred to leave several optional interpretations open than ignoring elements that disturb the clear picture. In spite of having answered the posed questions, new issues may emerge and others may prove less relevant than anticipated. It should always be considered whether others have reached similar results, which, however, should not necessarily be expected if their approaches have been far different. Checking the results and discussion with others, however, is always fruitful.

Explanatory power and transferability reflects good science, whether it is qualitative or quantitative. However, the qualitative researcher should not determine the external validity (whether results can be generalized to other contexts), but must ensure that the reader is given sufficient information to judge whether something can be transferred to another context. This requires a respect for complexity and ambiguity. New angles enabling the reader to view concepts or contexts in a new way may be relevant not only by suggesting options for action, but because it lets the players view their practices from new angles.

Qualitative research shares the values of aesthetics relating to factors such as structure and clarity with tension between clarity and ambiguity. Other values are rhetorical with compelling metaphors and space for the writer allowing for a glimpse of the human behind the text. The conviction and persuasive power in the arguments may be compared and weighed towards the arguments of others and thereby influence the scientific value of the study. One should also consider the pragmatic value in terms of the relevance of the study and for whom it is relevant.

The lack of a systematic approach and consensus on the validity criteria, and the difficulties with articulation of the integration theory in the working methods should be accepted as conditions, not as concerns, as long as the researcher is honest, especially about the special interests relating to one or the other conclusion. The lack of consensus may be a strength because readers maintain a skeptical view when reading the results.

Three examples illustrate how qualitative research the maritime setting may contribute to cognition:

• In a study of shipboard illness behavior, Bloor has shown that accommodation to symptoms was overlain by the economic imperative to keep the ship functioning. While management feared that the right to the sick role would allow malingering, the workers feared that adaption of the sick role would exclude them from employment [11].
• The imminent challenges that arise with regard to working at sea and psychosocial health problems have been recently reviewed after an IMHA workshop [12]. Many of these can only be approached by qualitative research methods.
• Fishermen’s’ job tasks and exposures to occupational hazards have been mapped by collecting and analyzing qualitative data [13, 14].