ComPASS: A software tool for Care Preference Assessment of Satisfaction in Skilled Nursing
Bibliographic record
Abstract
Abstract The results of the Compass project demonstrate both the feasibility and significant usefulness of digital resident preference assessments for enhancing person-centered care in nursing home communities. Using the scientifically validated Preferences for Everyday Living Inventory (PELI), Compass-21 was created, consisting of 21 targeted questions—16 aligned with MDS Section F and five additional high-priority preferences chosen by staff after reviewing the remaining 56 PELI questions. Focus groups and formative research with administrators and frontline staff revealed that existing paper-based and EHR systems were not dynamic enough and were poorly updated for sharing preference information, which hindered truly individualized care. Compass-21 enabled systematic, real-time documentation and sharing of residents’ preferences and satisfaction. In four nursing home communities, 72 eligible residents (with a Brief Inventory for Mental Status score of 12 or higher) completed assessments of their most important preferences and provided a satisfaction rating for how well each preference was met. The tool produced reports used in care planning meetings, facilitating targeted discussion and improvement efforts. A survey of the reports given to care staff (nursing, social work, activities), family, and residents showed that the reports were helpful (3.9/5.0), understandable (4.44/5.0), and useful (4.16/5.0). Statistical analyses examined the change in preference-congruent care by comparing baseline and 30-day follow-up using repeated measures ANOVA. These results showed positive increases in the number of important preferences that were met, providing a strong basis for expansion and wider adoption across communities.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".