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The ethical operational model developed in SotePeda 24/7 project in 2018-2020 has since its launch been used in continuous learning in social and health care. The model was developed to work as a tool in identification of ethical issues in the digitising operational environment of social and health care, as well as to support ethical evaluation and decision-making. When the ethical operational model is embraced, it results into internalizing the reflective process of the model and increases the ability to face ethical dilemmas in the mode of modus operandi indicating socio-culturally established ways of engaging and of doing things. Our aim in this study is to evaluate the experiences gained of the implementation of the ethical operational model in continuous learning in social and health care. As research methods we apply qualitative evaluation: program theory-based and systems approach. As material of the study, we address the experiences gained from the practical use of the model. Additionally, we scrutinize the ethical operational model as an artefact, as co-constructed object, and make use of the multifaceted data from the time of developing the model. The results of our study indicate that when the ethical operational model has been systematically used in continuous learning, the experiences have been overall positive, increasing awareness and ethical competence of the participants. From the point of view of its systemic impact, the scrutiny of the ethical operational model as an artefact, indicates that the focal constituents embedded enable even those new to the model to engage in it and to develop the content further via reflection. We conclude that the ethical operational model has found its way in active use as means of continuous learning in social and health care. The model may gradually become infused in digital era practices and be vitally present in action as modus operandi.

Alkaline pre-treatments were performed for the production of organics-containing effluents from silver/white birch (Betula pendula/pubescens) and Scots pine (Pinus sylvestris) chips prior to chemical pulping. Pre-treatment conditions were varied with respect to time (from 30 min to 120 min), temperature (130 °C and 150 °C), and alkali charge (1, 2, 3, 4, 6, and 8% of NaOH on oven-dried wood). The analytical data (total content, weight average molar mass, and molar mass distribution) on dissolved lignin were subjected to principal component analysis to examine the relationship between molar mass and molar mass distributions in lignin removed from different wood species under varying alkaline pre-treatment conditions. Using this method, differences between the wood species and effects of the various pre-treatment process variables (i.e., time, temperature, and alkali charge) were determined.

Autohydrolysis pre-treatments were performed for the production of hemicellulose-rich autohydrolysates from silver birch (Betula pendula) chips prior to chemical pulping. Pre-treatment conditions were varied with respect to time (from 30 to 120 min) and temperature (130 and 150 °C), covering a P-factor range from 10 to 238. Hydrolysates were analyzed in terms of carbohydrates, lignin, volatile organic acids, and furanoic compounds. The analytical data were subjected to various chemometric techniques to establish the relationships between dissolved organic components, hardwood and softwood used in the experiments, and applied pre-treatment conditions. Using this method, differences between the wood species could be clearly seen, and a relatively accurate model for the autohydrolysis of birch chips was developed.

Various chemometric techniques were used to establish the relationship between the autohydrolysis conditions prior to pulping and the chemical compositions of the soluble organic materials removed from Scots pine (Pinus sylvestris) wood chips. The aqueous chip pre-treatments (autohydrolysis) were administered at 130 °C and 150 °C for 30, 60, 90, and 120 min, and the hydrolysates obtained were characterized in terms of total carbohydrates (various mono-, oligo-, and polysaccharides together with uronic acid side groups), volatile acids (acetic and formic acids), lignin, and furans (furfural and 5-(hydroxymethyl)furfural). Based on the analytical data gathered, a relatively accurate model for pine chip autohydrolysis was developed.