Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

驱逐“懈怠症”

保持高水平的生产力,或者更进一步,将生产力提升到更高的层面,这往往是高级经理人们最为关注的问题之一。但有些经理人的举措却证明是适得其反的,其结果生产力不升反降。举例而言,人们有一种自然倾向,尤其对那些经验缺乏的经理,就是认为仔细监督下级的工作会让他们的工作效率更高。但如果一味依靠监督     

Spirituality and the care of patients at the end-of-life: an essential component of care

Spirituality is an essential component of the care of patients with serious illness and those that are dying. Dame Cicely Saunders developed the hospice movement based on the biopsychosocialspiritual model of care, in which all four dimensions are important in the care of patients. Of all the models of care, hospice and palliative care recognize the importance of spiritual issues in the care of patients and their families. The National Consensus Project Guidelines for Quality Palliative Care, in the United States, provides specific recommendations about all domains of care including the spiritual domain, which is recognized as a critical component of care (The National Consensus Project for Quality Palliative Care www.nationalconsensusproject.org). Studies indicate that the majority of patients would like their spiritual issues addressed, yet find that their spiritual needs are not being met by the current system of care. Interestingly, spirituality is the one dimension that seems to get slightly less emphasis than the biopsychosocial dimensions of care. Some reasons may include the difficulty with definitions of spirituality for clinical and research purposes, the time constraints and financial burdens in the current healthcare system in the United States, and the lack of uniform training for all healthcare professionals. Yet, there are theoretical and ethical frameworks that support spiritual care as well as some educational models in spirituality and health that have been successful in medical education in the United States. Spirituality can be seen as the essential part of the humanity of all people. It is at its root, relational and thus forms the basis of the altruistic care healthcare professionals are committed to. Spirituality has to do with respecting the inherent value and dignity of all persons, regardless of their health status. It is the part of humans that seeks healing, particularly in the midst of suffering. Spiritual care models are based on an intrinsic aspect that calls for compassionate presence to patients as well as an extrinsic component where healthcare professionals address spiritual issues with patients and their loved ones. Currently in the healthcare system, evidence-base models are the criteria for practice recommendations. Yet, spirituality may not be amenable entirely to strict evidence-base criteria. As hospice and palliative care continues to develop as a field, healthcare professionals are challenged to think of ways to advocate for and include the spiritual dimension of care.     

Does Iconicity in Pictographs Matter? The Influence of Iconicity and Numeracy on Information Processing,Decision Making,and Liking in an Eye‐Tracking Study

Researchers recommend the use of pictographs in medical risk communication to improve people's risk comprehension and decision making. However, it is not yet clear whether the iconicity used in pictographs to convey risk information influences individuals’ information processing and comprehension. In an eye‐tracking experiment with participants from the general population (N = 188), we examined whether specific types of pictograph icons influence the processing strategy viewers use to extract numerical information. In addition, we examined the effect of iconicity and numeracy on probability estimation, recall, and icon liking. This experiment used a 2 (iconicity: blocks vs. restroom icons) × 2 (scenario: medical vs. nonmedical) between‐subject design. Numeracy had a significant effect on information processing strategy, but we found no effect of iconicity or scenario. Results indicated that both icon types enabled high and low numerates to use their default way of processing and extracting the gist of the message from the pictorial risk communication format: high numerates counted icons, whereas low numerates used large‐area processing. There was no effect of iconicity in the probability estimation. However, people who saw restroom icons had a higher probability of correctly recalling the exact risk level. Iconicity had no effect on icon liking. Although the effects are small, our findings suggest that person‐like restroom icons in pictographs seem to have some advantages for risk communication. Specifically, in nonpersonalized prevention brochures, person‐like restroom icons may maintain reader motivation for processing the risk information.     

Evaluating the Simplified Conjoint Expected Risk Model: Comparing the Use of Objective and Subjective Information

The simplified Conjoint Expected Risk (CER) model by Holtgrave and Weber posits that perceived risk is a linear combination of the subjective judgments of the probabilities of harm, benefit, and status quo, and the expected harm and benefit of an activity. It modifies Luce and Weber's original CER model—that uses objective information to evaluate financial gambles—to accommodate activities such as health/technology activities where values of the model variables are subjective. If the simplified model is a valid modification of the original model, its performance should not be sensitive to the use of subjective information. However, because people may evaluate information differently when objective information is provided to them than when they generate information on their own, the performance of the simplified CER model may not be robust to the source of model-variable information. We compared the use of objective and subjective information, and results indicate that the estimates of the simplified CER model parameters and the proportion of variance in risk judgments accounted for by the model are similar under these two conditions. Thus, the simplified CER model is viable with activities for which harm and benefit information is subjective.     

Nonlinear structural equation modeling: is partial least squares an alternative?

Nonlinear structural equation modeling provides many advantages over analyses based on manifest variables only. Several approaches for the analysis of latent interaction effects have been developed within the last 15 years, including the partial least squares product indicator approach (PLS-PI), the constrained product indicator approach using the LISREL software (LISREL-PI), and the distribution-analytic latent moderated structural equations approach (LMS) using the Mplus program. An assumed advantage of PLS-PI is that it is able to deal with very large numbers of indicators, while LISREL-PI and LMS have not been investigated under such conditions. In a Monte Carlo study, the performance of LISREL-PI and LMS was compared to PLS-PI results previously reported in Chin et al. (2003) and Goodhue et al. (2007) for identical conditions. The latent interaction model included six indicator variables for the measurement of each latent predictor variable and the latent criterion, and sample size was N=100. The results showed that PLS-PI’s linear and interaction parameter estimates were downward biased, while parameter estimates were unbiased for LISREL-PI and LMS. True standard errors were smallest for PLS-PI, while the power to detect the latent interaction effect was higher for LISREL-PI and LMS. Compared to the symmetric distributions of interaction parameter estimates for LISREL-PI and LMS, PLS-PI showed a distribution that was symmetric for positive values, but included outlying negative estimates. Possible explanations for these findings are discussed.