Gaining access to unspoken narratives of people living with dementia on a hospital ward—A new methodology
Bibliographic record
Abstract
BACKGROUND: This is a methodological paper that aims to advance the conceptualisation of participatory research by focusing on the value of capturing and understanding movement as a vital means of communication for older people with dementia in a general hospital ward. Qualitative research involving people with dementia tends to be word-based and reliant upon verbal fluency. This article considers a method for capturing and understanding movement as a vital means of communication. METHOD: This narrative enquiry is underpinned by the model of social citizenship that recognises people with dementia as citizens with narratives to share. The study focused on spontaneously produced conversations that were video recorded and analysed through a lens of mobility. This enabled each participant to share what was important to them in that moment of time without always using words. FINDINGS: The study findings showed that people with dementia have narratives to share, but these narratives do not fit the bio-medically constructed model that is generally expected from patients. Utilising a mobilities lens enabled the narratives to be understood as containing layers of language. The first layer is the words; the second layer is gestures and movements that support the words; and the third layer is micro movements. These movements do not only support the words but in some cases tell a different story altogether. CONCLUSION: This methodology brings attention to layers of communication that reveal narratives as a mobile process that require work from both the teller and the listener to share and receive. Movements are shown to be the physical manifestations of embodied language which when viewed through a lens of mobility enable a deeper understanding of the experience of living with dementia when an inpatient. Viewing narratives through a mobilities lens is important to the advancement of dementia and citizenship practices.
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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 | 0.059 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".