Care planning needs of palliative home care clients: Development of the interRAI palliative care assessment clinical assessment protocols (CAPs)
Bibliographic record
Abstract
BACKGROUND: The interRAI Palliative Care (interRAI PC) assessment instrument provides a standardized, comprehensive means to identify person-specific need and supports clinicians to address important factors such as aspects of function, health, and social support. The interRAI Clinical Assessment Protocols (CAPs) inform clinicians of priority issues requiring further investigation where specific intervention may be warranted and equip clinicians with evidence to better inform development of a person-specific plan of care. This is the first study to describe the interRAI PC CAP development process and provide an overview of distributional properties of the eight interRAI PC CAPs among community dwelling adults receiving palliative home care services. METHODS: Secondary data analysis used interRAI PC assessments (N = 6,769) collected as part of regular clinical practice at baseline (N = 6,769) and follow-up (N = 1,000). Clients across six regional jurisdictions in Ontario, Canada, assessed to receive palliative homecare services between 2006 and 2011 were included (mean age 70.0 years; ±13.4 years). Descriptive analyses focused on the eight interRAI PC CAPs: Fatigue, Sleep Disturbance, Nutrition, Pressure Ulcers, Pain, Dyspnea, Mood Disturbance and Delirium. RESULTS: The majority of clients triggered at least one CAP while two thirds triggered two or more. Triggering rates ranged from 74% for the Fatigue CAP to less than 15% for the Delirium and Pressure Ulcers CAPs. The hierarchical CAP triggering structure suggested Fatigue and Dyspnea CAPs were persistent issues prevalent among the majority of clients while Delirium and Pressure Ulcers CAPs rarely trigger in isolation and most often trigger later in the illness trajectory. CONCLUSION: When any of the eight interRAI PC CAPs are triggered, clinicians should take notice. CAPs triggered at high rates such as fatigue, dyspnea, and pain warrant increased attention for the majority of clients. Consideration of triggered CAPs provide evidence to inform a collaborative decision making process on whether or not issues raised by the CAPs should be addressed in the plan of care. Integrating evidence from the interRAI PC CAPs into the clinical decision making process support care planning to address client strengths, preferences and needs with greater acuity.
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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 | 0.077 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".