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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".