The scientific literature does not reflect a shared, integrated vision of the exact nature and dimensions of work (dis)ability. However, except for a few definitions, there seems to be a consensus that work (dis)ability is a relational concept resulting from the interaction of multiple dimensions that influence each other through different ecological levels. The conceptualization of work (dis)ability also seems to have become more dynamic over time. The way work (dis)ability is defined has important implications for research, compensation and rehabilitation.
Knowing costs, duration and predictors of long-term compensation claims by gender can help employers, decision makers and rehabilitation specialists to identify at-risk workers and industries to engage them in early intervention and prevention programmes. Tailoring parts of long-term disability prevention and management efforts to men's and women's specific needs, barriers and vulnerable subgroups, could reduce time on benefits among both male and female long-term claimants.
Background
The mortality rate from breast cancer has been declining for many years, and the population size of working-age survivors is steadily increasing. However, the recurrent side effects of cancer and its treatment can result in multiple disabilities and disruptions to day-to-day life, including work disruptions. Despite the existing knowledge of best practices regarding return to work (RTW) for breast cancer survivors, only a few interdisciplinary interventions have been developed to address the individualized needs and multiple challenges of breast cancer survivors, health care professionals, and employer and insurer representatives. Thus, it seems appropriate to develop RTW interventions collaboratively by using a co-design approach with these specific stakeholders.
Objective
This paper presents a protocol for developing and testing an innovative, interdisciplinary pilot intervention based on a co-design approach to better support RTW and job retention after breast cancer treatment.
Methods
First, a participatory research approach will be used to develop the intervention in a co-design workshop with 12 to 20 participants, including people affected by cancer, employer and insurer representatives, and health care professionals. Next, a pilot intervention will be tested in a primary care setting with 6 to 8 women affected by breast cancer. The acceptability and feasibility of the pilot intervention will be pretested through semistructured interviews with participants, health care professionals, and involved patient partners. The transcribed data will undergo an iterative content analysis.
Results
The first phase of the project—the co-design workshop—was completed in June 2021. The pilot test of the intervention will begin in spring 2022. The results from the test will be available in late 2022.
Conclusions
The project will offer novel data regarding the use of the co-design approach for the development of innovative, co-designed interventions. In addition, it will be possible to document the acceptability and feasibility of the pilot intervention with a primary care team. Depending on the results obtained, the intervention could be implemented on a larger scale.
International Registered Report Identifier (IRRID)
DERR1-10.2196/37009
Taking account of sex and gender in occupational health studies poses statistical challenges. Other sociodemographic variables, such as racialization, class, and age, also affect the relations between workplace exposures and health and interact with sex and gender. Our objective was to perform a critical review of conventional and emerging statistical tools, examining whether each analysis takes account of sociodemographic variables (1) in a way that contributes to identification of critical occupational determinants of health (2) while taking account of relevant population characteristics to reflect intersectional approaches to health and (3) using sample sizes and population characteristics available to researchers. A two-step search was conducted: (1) a scientific watch concerning the statistical tools most commonly used in occupational health over the past 20 years; (2) a screening of the 1980–2022 literature with a focus on emerging tools. Our examination shows that regressions with adjustment for confounders and stratification fail to reveal the sociodemographic mechanisms that interact with occupational health problems, endangering the identification of occupational risks. Multilevel (notably MAIHDA) analyses, decision tree, cluster, and latent analyses are useful methods to consider when seeking to orientate prevention. Researchers should consider methods that adequately reveal the mechanisms connecting sociodemographic variables and occupational health outcomes.
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