Taking the Next Steps in Goal Ascertainment: A Prospective Study of Patient, Team, and Family Perspectives Using a Comprehensive Standardized Menu in a Geriatric Assessment and Treatment Unit
Bibliographic record
Abstract
OBJECTIVES: To examine the performance of an expanded menu of goals in a geriatric rehabilitation setting incorporating patient, family, and team perspectives. DESIGN: Prospective comparative study. SETTING: Inpatient geriatric rehabilitation unit. PARTICIPANTS: Nineteen consecutive eligible inpatients and their family members and seven unit team members. MEASUREMENTS: Each participant independently reviewed a standardized menu of medical, functional, psychosocial, spiritual, and future planning goals, which were compared with the goals set by the healthcare team. RESULTS: On average, patients and their family members agreed on goals about half the time; family members tended to have more goals than patients. Agreement between the team and patients and families was poor (kappas=-0.11-0.33). The team was less likely to identify functional, psychosocial, spiritual, and future planning goals. CONCLUSION: Patients and families have a broad range of goals that are not always identified by the healthcare team. A comprehensive standardized menu is feasible and may be helpful in setting goals in a geriatric rehabilitation setting.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".