Improving Dementia Home Caregiving and Restructuring the Dementia Narrative Through Creating a Graphic Memoir and Engaging in a Psychoanalytic Narrative Research Method
Bibliographic record
Abstract
Informal dementia home caregiving is viewed negatively by society and can result in caregiver depression and anxiety from burnout, potentially compromising caregiving. Caregiver creation of a graphic memoir may help to mitigate the negative dementia narrative while engaging in it, and a psychoanalytic narratology method may reduce experienced depression and anxiety associated with burnout. This investigation examines writing, illustrating, and publishing a graphic memoir by one informal dementia home caregiver. As the mother of the illustrator and the editor and publisher of this graphic memoir, I provide the perspective of this investigation based on communications with the author and illustrator. My historical analysis, in which the author participated, represents psychoanalytic narrative research, serving as the historical method. The effects of writing, illustrating, and publishing the graphic memoir were able to reduce the informal dementia home caregivers’ symptoms during the entire process and extend the effect of this endeavor until the death of the mother. Engaging in the psychoanalytic narrative research process was additionally effective in this regard. The outcomes demonstrate the viability of writing and illustrating a publishable graphic memoir for other informal dementia home caregivers and the possibility of it and the narrative research method to help decrease their depression and anxiety regarding burnout.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".