Youyou et al. showed that from 70 likes the algorithm could predict the personality better than friends, from 150 likes better than family members and from 300 likes even better than the test person himself. However, the machine learning algorithm does not know the person better than the colleagues, the friends or the person themselves. The machine can "only", after sufficient "supervised learning" trials (iterations), determine the correlation between the click behaviour on Facebook and the scored Big5 factors better than individuals. Prediction replaces the Big5 questionnaire. But we are not getting closer to the personality of people than with the Big5 questionnaire. It is argued that - though data mining can help enormously - psychology remains a subject of the narrative in the end.
MULTIFILE
The human-technology symbiosis has brought us prosperity and adversity, and through the massive scaling that has arisen, we now face global problems (environment, climate, technological totalitarianism / technological iatrogenesis, lack of meaning) that we can’t solve with our nearly 300,000 year old psycho-biological “hardware”.
MULTIFILE
Objective To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors. Design A prospective cohort study with a 6-month follow-up between January 2020 and March 2023. Setting 30 physiotherapy primary care practices. Participants Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset. Baseline measures Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude. Outcome measures Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain. Results 62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer–Lemeshow test yielded a p-value of 0.7167, indicating a good model fit. Conclusion This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.
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The building industry is a major target for resource-efficiency developments, which are crucial in European Union’s roadmaps. Using renewable materials impacts the sustainability of buildings and is set as urgent target in current architectural practice. The building industry needs renewable materials positively impacting the CO2 footprint without drawbacks. The use of wood and timber as renewable construction materials has potentials, but also drawbacks because trees need long time to grow; producing timber generates considerable waste; and the process from trees to applications in buildings requires transportation and CO2 emission. This research generates new scientific knowledge and a feasibility study for a new wood-like bio-material - made of cellulose and lignin from (local) residual biomass via i.e. 3D printing - suitable for applications in the building industry. It contributes to a sustainable built environment as it transforms waste from different sectors into a local resource to produce a low carbon-footprint bio-material for the construction sector. Through testing, the project will study the material properties of samples of raw and 3D printed material, correlating different material recipes that combine lignin and cellulose and different 3D printing production parameters. It will map the material properties with the requirements of the construction industry for different building products, indicating potentials and limits of the proposed bio-material. The project will produce new knowledge on the material properties, a preliminary production concept and an overview of potentials and limits for application in the built environment. The outcome will be used by industry to achieve a marketable new bio-material; as well as in further scientific academic research.
