"Farmers Aren't into the Emotions and Things, Right?": A Qualitative Exploration of Motivations and Barriers for Mental Health Help-Seeking among Canadian Farmers.

Working in agriculture has been associated with an increased prevalence of psychological distress and mental health concerns. Farmers are also less likely than non-farmers to seek-help for their mental health. Previous research examining help-seeking among farmers has focused predominantly on male farmers, and has not included many of the Canadian agricultural commodity groups or provinces. The goal of this study was to explore perceptions of farmer help-seeking for mental health amongst farmers and people who work with farmers. The study objectives were to characterize the motivations and barriers to help-seeking behaviours. Semi-structured interviews were conducted with 75 farmers and individuals who work with farmers in Ontario, Canada, between 2017 and 2018. Interviews were conducted in person, and by telephone when needed. Topics of discussion included farming stresses and their impacts; personal well-being; agricultural crises and mental health help-seeking; use of mental health supports; motivators and barriers to help-seeking; and perceived ideals for mental health supports. Thematic analysis was conducted collaboratively by three authors using inductive and deductive coding. Our analysis resulted in five themes around help-seeking motivations and barriers: 1) Accessibility of mental health supports and services; 2) Stigma around mental health in the agricultural community; 3) Anonymity and/or lack of anonymity in seeking support; 4) Farm credibility; and 5) Recommendations for implementing mental health services for the agricultural community. This study provides insights around how farming culture and the accessibility and delivery of services may influence help-seeking for mental health, and proposes strategies to break down barriers to help-seeking in this population.

What Impacts Perceived Stress among Canadian Farmers? A Mixed-Methods Analysis.

Globally, farmers report high levels of occupational stress. The purpose of this study was to identify and explore factors associated with perceived stress among Canadian farmers. A sequential explanatory mixed-methods design was used. An online cross-sectional national survey of Canadian farmers (n = 1132) was conducted in 2015-2016 to collect data on mental health, demographic, lifestyle, and farming characteristics; stress was measured using the Perceived Stress Scale. A multivariable linear regression model was used to investigate the factors associated with perceived stress score. Qualitative interviews (n = 75) were conducted in 2017-2018 with farmers and agricultural sector workers in Ontario, Canada, to explore the lived experience of stress. The qualitative interview data were analyzed via thematic analysis and then used to explain and provide depth to the quantitative results. Financial stress (highest category-a lot: (B = 2.30; CI: 1.59, 3.00)), woman gender (B = 0.55; CI: 0.12, 0.99), pig farming (B = 1.07; CI: 0.45, 1.69), and perceived lack of support from family (B = 1.18; CI: 0.39, 1.98) and industry (B = 1.15; CI: 0.16-2.14) were positively associated with higher perceived stress scores, as were depression and anxiety (as part of an interaction). Resilience had a small negative association with perceived stress (B = -0.04; CI: -0.06, -0.03). Results from the qualitative analysis showed that the uncertainty around financial stress increased perceived stress. Women farmers described the unique demands and challenges they face that contributed to their overall stress. Results from this study can inform the development of mental health resources and research aimed at decreasing stress among Canadian farmers.

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction.

PURPOSE: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. METHODS: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. RESULTS: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. CONCLUSIONS: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

Proximal ADMM for multi-channel image reconstruction in spectral X-ray CT.

The development of spectral X-ray computed tomography (CT) using binned photon-counting detectors has received great attention in recent years and has enabled selective imaging of contrast agents loaded with K-edge materials. A practical issue in implementing this technique is the mitigation of the high-noise levels often present in material-decomposed sinogram data. In this work, the spectral X-ray CT reconstruction problem is formulated within a multi-channel (MC) framework in which statistical correlations between the decomposed material sinograms can be exploited to improve image quality. Specifically, a MC penalized weighted least squares (PWLS) estimator is formulated in which the data fidelity term is weighted by the MC covariance matrix and sparsity-promoting penalties are employed. This allows the use of any number of basis materials and is therefore applicable to photon-counting systems and K-edge imaging. To overcome numerical challenges associated with use of the full covariance matrix as a data fidelity weight, a proximal variant of the alternating direction method of multipliers is employed to minimize the MC PWLS objective function. Computer-simulation and experimental phantom studies are conducted to quantitatively evaluate the proposed reconstruction method.

Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT.

The development of spectral computed tomography (CT) using binned photon-counting detectors has garnered great interest in recent years and has enabled selective imaging of K-edge materials. A practical challenge in CT image reconstruction of K-edge materials is the mitigation of image artifacts that arise from reduced-view and/or noisy decomposed sinogram data. In this note, we describe and investigate sparsity-regularized penalized weighted least squares-based image reconstruction algorithms for reconstructing K-edge images from few-view decomposed K-edge sinogram data. To exploit the inherent sparseness of typical K-edge images, we investigate use of a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ1-norm with a wavelet sparsifying transform. Computer-simulation and experimental phantom studies are conducted to quantitatively demonstrate the effectiveness of the proposed reconstruction algorithms.