A model for using the VIPS framework for person‐centred care for persons with dementia in nursing homes: a qualitative evaluative study
Bibliographic record
Abstract
røsvik j., kirkevold m., engedal k., brooker d. & kirkevold ø. (2011) A model for using the VIPS framework for person‐centred care for persons with dementia in nursing homes: a qualitative evaluative study. International Journal of Older People Nursing6, 227–236 doi: 10.1111/j.1748‐3743.2011.00290.x Background. The ‘VIPS’ framework sums up the elements in Kitwood’s philosophy of person‐centred care (PCC) for persons with dementia as values, individualised approach, the perspective of the person living with dementia and social environment. There are six indicators for each element. Aim. To conduct an initial evaluation of a model aimed at facilitating the application of the VIPS framework. Design. Qualitative evaluative study. Methods. A model was trialled in a 9‐week pilot study in two nursing homes and evaluated in four focus groups using qualitative content analysis. Results. Five themes emerged: (1) Legitimacy of the model was secured when central roles were held by nurses representing the majority of the staff; (2) The model facilitated the staff’s use of their knowledge of PCC; (3) Support to the persons holding the internal facilitating roles in the model was needed; (4) The authority of the leading registered nurse in the ward was crucial to support the legitimacy of the model and (5) Form of organisation seemed to be of importance in how the model was experienced. Conclusion. The model worked best in wards organised with a leading registered nurse who could support an auxiliary nurse holding the facilitating function.
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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 | 0.089 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".