‘Ask a hundred people, you get a hundred definitions’: A comparison of lay and expert understanding of stress and its associations with health

The understanding an individual holds about stress can influence their appraisal of it and have implications for subsequent health, yet knowledge of such understanding is scarce. This study explored discrepancies between lay and expert understanding of stress and links made between stress and health. Twenty-six lay members of the local community aged 18–62 years, and seven expert stress researchers, participated in individual semi-structured interviews. Thematic analysis of the two datasets was conducted separately, then findings compared to identify similarities and differences between lay and scientific understanding. Whilst many similarities were identified, we found three important discrepancies: (i) Lay participants demonstrated a strong awareness of the indirect effects of stress on health via health behaviours; (ii) compared to experts, lay participants showed less awareness of a direct path between stress and physical health; (iii) lay participants showed less understanding of social determinants of stress and collective measures for stress management that went beyond individual responsibility. Discrepancies identified serve to highlight potential misunderstandings in lay conceptualisation of stress and its links with health. These findings have potential to facilitate the work of practitioners who serve as intermediaries to translate scientific knowledge into therapeutic benefit, through improved awareness and communication surrounding stress understanding

The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons

Undoing Gendered Identities? Centrality and Meanings of Parental and Work Identities in Semi-Traditional, Equal-Sharing and Role-Reversed Couples

This mixed-methods study explored the centrality and meanings of men’s and women’s parental and work-related identities by comparing semi-traditional, equal-sharing, and role-reversed couples. Quantitative analysis involved 2,813 British parents (1,380 men, 1,433 women) who were primary caregivers, primary breadwinners, or equal sharers with at least one child aged 11 or under. Qualitative analysis drew on 60 in-depth interviews with 10 couples from each of the three groups. Results indicated that the centrality of parental and work identities varied by role rather than gender, as both male and female caregivers reported less central work identities and more central parental identities compared to breadwinners and equal-sharers. Equal-sharers and role-reversers were characterized by women’s central work identity and men’s low centrality of work identity. In these couples, a `half and half` parenting ideology underlined the construction of mothering and fathering as equivalent interchangeable identities, each forming only one half of a child’s parenting. Intertwining their maternal identity with an equivalent construction of their partners’ identity allowed women to reconcile a good mother ideal with central work identities, by redefining mothering as a responsibility for only half of the caregiving