Facilitating resident community in nursing homes: a slippery slope? An analysis on collectivistic and individualistic approaches
Bibliographic record
Abstract
Residents in nursing homes are old and frail and are dependent on constant care, medical, or otherwise, by trained professionals. But they are also social beings, secluded in an institutional setting which is both total and foreign. In this setting, most of the residents most of the time must relate to other residents: other residents are the nursing home residents' peers, companions, and perhaps even significant others. In this article, we will discuss how resident communities in nursing homes are influenced by the approaches of nursing home staff. Two nursing homes have been included in this article-one from Canada and one from Norway. Participant observation was conducted at these two nursing homes, predominantly focusing on everyday-life activities. The cases from Norway and Canada are illustrative of two very different general approaches to residents: one collectivistic and one individualistic. These general approaches produce different contexts for the formation and content of resident communities, greatly affecting nursing home residents. The significance of these approaches to resident community is profound and also somewhat unanticipated; the approaches of staff provide residents with different opportunities and limitations and also yield unintended consequences for the social life of residents. The two different general approaches are, we suggest, "cultural expressions," conditioned by more than official preferences and recommendations. The difference between the institutions is, in other words, anchored in ideas and ideologies that are not explicitly addressed.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".