DATA SET for: 'Misdiagnosed and misunderstood'- Poetry as a co-created research methodology across geographical boundaries for rarer dementias: The electronic poems
Bibliographic record
Abstract
This data set contains 27 completed poems from 71 participants (9 cohort groups), explanations of the poets’ creative process, and source material (original words) from people living with rare dementia and carers who responded to a series of three prompts over a 12 week period. Data was collected between 2021-2022 as part of the Electronic Poems Project within the Rare Dementia Support Impact Study. Study Abstract Purpose: Poetry can convey sensory and emotional information and is a way to understand complex phenomena. This study explored the development of a new co-created form of poetic inquiry to further comprehend the lived experiences of people affected by 6 rarer dementias. These include young onset, inherited and non-memory-led conditions that are often misunderstood and subsequently lack care and support. Methods: Three prompts over about 12 weeks were sent electronically to 71 international participants to solicit responses, which were thematically analysed, creating 27 group poems. Follow-up surveys using content analysis assessed participant experiences producing and responding to the poems. Results: Analysis resulted in 3 - 4 themes per prompt, conveying very difficult aspects of lived experience, often owing to atypical symptoms, younger onset, misunderstandings by professionals and others, lack of support pathways, tremendous future uncertainty and a continuous struggle to adapt. Survey results found 74% had a positive experience contributing to the poems whilst 84% responded positively to the completed poems. Conclusions: As one of the largest empirical poetry-based studies that we are aware of, this novel, accessible approach of co-creating group poems yielded support for poetry as an arts-based qualitative research methodology that was able to gather substantial in-depth information about the experiences and needs of those affected by rarer dementias. Survey responses provided additional significant support for this methodological approach. Future research is suggested. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is part of the Rare Dementia Support Impact Project (The impact of multicomponent support groups for those living with rare dementias, (ES/S010467/1)) and is funded jointly by Economic and Social Research Council, part of UK Research and Innovation, and the National Institute for Health Research (UK). The views expressed are those of the authors and not necessarily those of the ESRC, UKRI, the NIHR or the Department of Health and Social Care. Rare Dementia Support is generously supported by the National Brain Appeal https://www.nationalbrainappeal.org/). Lead investigator S. J. Crutch and co-investigators: J. Stott, P. M. Camic, G. Windle, R. Tudor-Edwards, Z. Hoare, M.P. Sullivan and R. McKee-Jackson.
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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 | 0.014 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.015 |
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".