Implementing the interRAI Check-Up Comprehensive Assessment: Facilitating Care Planning and Care Coordination during the Pandemic
Bibliographic record
Abstract
Background: Long-stay home care patients are a large population of older adults with multi-morbidity and frailty. The COVID-19 pandemic posed challenges to executing care coordination and completing in-home assessments due to provincial mandates restricting in-person care. We evaluated the implementation of the interRAI Check-Up Self-Report instrument administered by phone and video. Methods: We report on a mixed-methods study, which involved the collection and analysis of survey and focus group data. Care coordinators from two regions in Ontario who had implemented the Check-Up at least once between March 2020 to September 2021 were recruited via convenience sampling. Results: A total of 48 survey respondents and 7 focus group participants consented to the study. Advantages of completing the Check-Up over the telephone or video call included: reduced travel time, reduced risk of disease transmission, familiarity with the assessment questions, and reduced time spent administering the assessment. Limitations most frequently reported were: the inability to see the living environment, hearing impairments, inability to observe non-verbal responses or cues, language barriers, difficulty building rapport, and difficulty understanding the patient. Conclusions: The Check-Up was advantageous in providing sufficient information to create a care plan when administered over the phone and by video. Implementation of the Check-Up assessment was facilitated by familiarity and alignment with other interRAI assessments. Our results indicate that population characteristics need to be taken into consideration for administration of self-report style of assessments.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".