Original ResearchGender-related explanatory models of depression: A critical evaluation of medical articles
Introduction
Epidemiological research on the prevalence and incidence of depressive symptoms and unipolar depressive disorders has consistently shown a preponderance in women compared with men.1 The female:male ratio varies with age, and the higher prevalence of depression among women has been shown from mid-puberty throughout adult life.2 No significant gender differences have been found in relation to bipolar depressive disorders.3
The reasons for the gender differences in unipolar depressive disorders are still not adequately understood.4 Medical reviews have been written about gender differences in depression2, 5, 6, 7 with the aim of analysing different explanations for the higher prevalence of depression in women compared with men. Different explanatory models such as artefact, genetic, hormonal, psychological and sociocultural factors have been suggested, but consensus is lacking regarding explanations for the higher prevalence of depression in women compared with men.6 An increasing number of researchers acknowledge that depression must be understood from a multifactorial perspective.7, 8 In a scientific evaluation of research on depression, the Swedish Council on Technology Assessment in Health Care9 came to the conclusion that sociocultural factors (such as adverse experiences in childhood) in combination with socialization processes and psychological factors (such as vulnerability to adverse life events and coping skills) were the most important explanatory factors for gender differences in depression. It was also concluded that genetic factors did not appear to contribute to women's increased risk for depression, while hormonal factors could have some effect but to a lesser extent than environmental factors; furthermore, there was uncertainty about neurotransmitter systems and the adrenal/thyroid axis.6, 9
While reviews of scientific articles try to evaluate the scientific evidence for different explanations, to the authors' knowledge, no studies have tried to analyse which of the explanatory factors for the higher prevalence of depression in women dominate the medical discourse. The medical discourse can be analysed in articles indexed in the medical database PubMed. It is important to analyse domination in quantitative terms because the most commonly presented explanations may influence readers, which can have both scientific and practical consequences. Some years ago, Piccinelli and Wilkinson published a review in favour of sociocultural and psychological explanations.6 A question that remains to be answered is whether domination of these explanatory models can be found in the medical literature some years after publication. In such an analysis, it is of interest to study both the prevalence of different explanatory models to gender differences in depression, and the scientific quality of the different models.
There is no agreement regarding how to measure the quality of research. One method that is increasingly used as a tool for scientific evaluation of research is bibliometric, especially citation analyses and journal impact factors.10, 11 However, criticism has been raised regarding these quantitative ways of measuring the scientific quality of research. Wallin claims that a true assessment of scientific quality cannot be obtained by analysing a publication's citation number or journal impact factor.12 Such an assessment should also include peer review of the societal effects of research. However, Wallin gives no information about what he means by societal effects or how they could be analysed in more detail. One possible societal effect of research on gender and depression is the risk of over-simplifying and exaggerating the results.13 If women as a group are portrayed as depressed, while men as a group are described as not depressed, there is a risk of essentialism; that is the tendency to regard differences between men and women as constant, pervasive and unchangeable.14 Different methods can be used in order to diminish the risk for over-generalization. One way is to use an intersectional framework in making differences visible within the group of women (and within the group of men) with regard to class, race, ethnicity, age, sexual orientation, religion and other power-related dimensions.15 Another way of decreasing the risk of essentialism is to use a multifactorial framework for understanding the complex relationship between gender and depression.
The present study measured the scientific quality of medical articles indexed in PubMed with bibliometric measures and also with two questions about intersectionality and multifactorial dimensions. In this study, the term ‘gender-related model’ is used for models that try to explain the higher incidence of depression in women compared with men, and models used in single-gender analyses (why depression occurs in men, why depression occurs in women).
The aim of this paper was to analyse the prevalence and the scientific quality of different gender-related explanatory models of depression in the medical database PubMed.
The following research questions were analysed with regard to the medical articles:
- 1.
What gender-related explanatory models were given? In how many of the articles were the explanatory models used?
- 2.
Does the scientific quality differ between the explanatory models? The quality was measured with the following questions:
- a.
Is more than one possible gender-related explanatory model discussed?
- b.
Are differences within the group of men and within the group of women analysed in relation to socio-economic status, ethnicity, sexual orientation, etc. (except for age)?
- c.
What are the mean impact factors of the journals and the mean citation numbers of the different articles in each main explanatory model?
- a.
Section snippets
Methods
The database used in this study was PubMed (the US National Library of Medicine biomedical publication database, including citations from MEDLINE and other life science journals for biomedical articles). The database search took place in December 2003 and covered 2002. Articles were selected as described.
The following search criteria were used in a search in the title section, English language and the descriptors: Depress* AND (sex OR wom* OR gender OR man OR men OR female OR male OR feminis*).
Results
The codes, the subcategories and the categories are described in Table 1.
Overall, 10 subcategories (gonadal hormones, genetic factors, other biological factors, life circumstances, cultural factors, psychological factors, behaviour, sexual orientation, body image, measurement bias) and four categories (biomedical model, socio-cultural model, psychological model, artefact) were identified. The majority of codes and subcategories were found in articles on depression in women (most of them focused
Discussion
The main finding was that the most commonly used gender-related model was the biomedical model, which scored highest on bibliometric measures but lowest when multifactoriality and intersectionality were analysed.
Conclusions
The biomedical model, compared with the sociocultural and psychological models, seemed to have greater prominence within the medical discourse in explaining gender-related aspects of depression. However, the biomedical model scored lower than the sociocultural and psychological models when multifactoriality and intersectionality were analysed. There is a need to develop new methods for evaluation of the scientific quality of research.
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