Data sharing between home care professionals: a feasibility study using the RAI Home Care instrument
Bibliographic record
Abstract
BACKGROUND: Across Ontario, home care professionals collect standardized information on each client using the Resident Assessment for Home Care (RAI-HC). However, this information is not consistently shared with those professionals who provide services in the client's home. In this pilot study, we examined the feasibility of sharing data, from the RAI-HC, between care coordinators and service providers. METHODS: All participants were involved in a one-day training session on the RAI-HC. The care coordinators shared specific outputs from the RAI-HC, including the embedded health index scales, with their contracted physiotherapy and occupational therapy service providers. Two focus groups were held, one with care coordinators (n = 4) and one with contracted service providers (n = 6). They were asked for their opinions on the positive aspects of the project and areas for improvement. RESULTS: The focus groups revealed a number of positive outcomes related to the project including the use of a falls prevention brochure and an increased level of communication between professionals. The participants also cited multiple areas for improvement related to data sharing (e.g., time constraints, data being sent in a timely fashion) and to their standard practices in the community (e.g., busy workloads, difficulties in data sharing, duplication of assessments between professionals). CONCLUSIONS: Home care professionals were able to share select pieces of information generated from the RAI-HC system and this project enhanced the level of communication between the two groups of professionals. However, a single information session was not adequate training for the rehabilitation professionals, who do not use the RAI-HC as part of normal practice. Better education, ongoing support and timely access to the RAI-HC data are some ways to improve the usefulness of this information for busy home care providers.
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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 | 0.106 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".