15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Quantitative methods for group bibliotherapy research: a pilot study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background: Bibliotherapy is under-theorized and under-tested: its purposes and implementations vary widely, and the idea that ‘reading is good for you’ is often more assumed than demonstrated. One obstacle to developing robust empirical and theoretical foundations for bibliotherapy is the continued absence of analytical methods capable of providing sensitive yet replicable insights into complex textual material. This pilot study offers a proof-of-concept for new quantitative methods including VAD (valence–arousal–dominance) modelling of emotional variance and doc2vec modelling of linguistic similarity.

          Methods: VAD and doc2vec modelling were used to analyse transcripts of reading-group discussions plus the literary texts being discussed, from two reading groups each meeting weekly for six weeks (including 9 participants [5 researchers (3 authors, 2 collaborators), 4 others] in Group 1, and 8 participants [2 authors, 6 others] in Group 2).

          Results: We found that text–discussion similarity was inversely correlated with emotional volatility in the group discussions (arousal: r = -0.25; p = ns; dominance: r = 0.21; p = ns; valence: r = -0.28; p = ns), and that enjoyment or otherwise of the texts and the discussion was less significant than other factors in shaping the perceived significance and potential benefits of participation. That is, texts with unpleasant or disturbing content that strongly shaped subsequent discussions of these texts were still able to sponsor ‘healthy’ discussions of this content, as evidenced by the combination of low arousal plus high dominance despite low valence in the emotional qualities of the discussion.

          Conclusions: Our methods and findings offer for the field of bibliotherapy research both new possibilities for hypotheses to test, and viable ways of testing them. In particular, the use of natural language processing methods and word norm data offer valuable complements to intuitive human judgement and self-report when assessing the impact of literary materials.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Glove: Global Vectors for Word Representation

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

            We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Efficient Estimation of Word Representations in Vector Space

              We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data CurationRole: InvestigationRole: MethodologyRole: Project AdministrationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Funding AcquisitionRole: InvestigationRole: MethodologyRole: Project AdministrationRole: Writing – Review & Editing
                Role: Formal AnalysisRole: MethodologyRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Research
                F1000 Research Limited (London, UK )
                2398-502X
                7 March 2022
                2022
                : 7
                : 79
                Affiliations
                [1 ]The Oxford Research Centre for the Humanities, University of Oxford, Oxford, UK
                [2 ]Margaret Beaufort Institute of Theology, University of Cambridge, Cambridge, UK
                [3 ]The London Interdisciplinary School, London, UK
                [1 ]Universitat Basel, Basel, Basel-Stadt, Switzerland
                Brunel University London, UK
                [1 ]Westerdals School of Communication, Kristiania University College, Oslo, Norway
                Brunel University London, UK
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: None to disclose.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0003-1628-1700
                https://orcid.org/0000-0001-6064-7867
                Article
                10.12688/wellcomeopenres.17469.1
                10905136
                38435449
                3f191224-e32b-4b3f-9244-20bed1f0765a
                Copyright: © 2022 Troscianko ET et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 January 2022
                Funding
                Funded by: Wellcome Trust
                Award ID: 205493
                Funded by: Balliol Interdisciplinary Institute
                This work was supported by Wellcome [205493; a fellowship awarded to James Carney]; and a grant from the Balliol Interdisciplinary Institute awarded to Emily Holman.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles

                bibliotherapy,evaluation,group reading,narrative,literature,linguistic analysis

                Comments

                Comment on this article