Bibliographic record
Abstract
The experience of chronic illness is often emotionally challenging. People respond to these challenges in very different ways yet their responses are inevitably shaped by their resources and social position, the wider environment and clinical, personal and societal reactions to their symptoms and diagnosis (or lack of diagnosis). This special issue of Chronic Illness features six papers, which all consider how people experiencing chronic illness talk about, and live with, emotions in different cultural contexts. We include papers from researchers in four countries: Japan, Israel, Germany and the UK. All of the researchers are members of the DIPEx International (DI) collaboration of qualitative health researchers and health professionals who have been inspired by the UK project, which produces www.healthtalkonline.org and www.youthhealthtalk.org. The first UK collections were published in 2001 and now include over 70 different health and illness sections. Similar studies have been conducted for sister websites in Australia, Japan, Korea, Spain and Germany. Projects are underway in the Netherlands, Israel and Canada and planned in many other countries. All of the DI projects use the same qualitative research methods: each collection involves a diverse, maximum variation sample of interviews, collected and analysed by experienced academic social scientists. Researchers travel throughout their country conducting digital video or audio (depending on the participant’s preference) recorded interviews with between 40 and 50 participants. People are interviewed at a place of their choice, usually their home; we avoid clinical settings. The interviews share a similar structure. First, the researcher invites the participant to tell the story of what has happened since, for example, they first suspected a problem. When the respondent indicates that this, the ‘narrative section’, is complete (in our experience this might take anything from 5min to 5 h) the researcher uses prompts and questions in a semi-structured section of the interview. Sometimes more than one interview is needed. The narrative section of the interview is intended to provide a space for the participant to tell their story and to identify the participants’ own priorities, rather than to elicit responses to a more narrowly focussed agenda. Cultural variations in responses to this kind of interview are likely but, while the research interview is arguably a western construction, storytelling is a feature of all human societies. Chronic Illness 8(3) 159–162 ! The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1742395312451281 chi.sagepub.com
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".