“Everybody Has Different Levels of Why They Are Here”: Deconstructing Domestication in the Nursing Home Setting
Bibliographic record
Abstract
This chapter uses a disability studies perspective to deconstruct the relationship between personhood and domestication, as this relationship has been made to appear in interviews with older adults residing in a nursing home in Canada. Our analysis illustrates how assumptions about dementia shape the self-perceptions, experiences of home, and meaning of co-residence for people living in the nursing home setting. Tom Kitwood’s conception of person-centered dementia care is examined as a means of understanding how personhood is imagined within the context of residential dementia care from the perspective of residents without dementia . We trace contradictions in the ways nursing home residents with dementia are imagined and treated as patients rather than persons in what we call “governing texts.” These texts operate at multiple levels, and include global policies, institutional reports, and individual stories. Dominant understandings of dementia treat dementia as either a threat to personhood, and to being and feeling “at home,” or as an opportunity to reaffirm the significance of personhood to being and feeling “at home.” This chapter concludes by offering a counternarrative of dementia that orients to dementia as an expression of “faith in reciprocity and a shared life” (van Manen 1990, 16). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".