Developing a Supplemental Assessment Tool for Younger Residents in Long-Term Care
Bibliographic record
Abstract
BACKGROUND: It has been established that the needs of long-term care residents under 65 are distinct from those of older residents, and that these needs are not sufficiently met through the current model of LTC. Our goal was to create a supplemental assessment tool that can be used at the time of assessment to better represent the needs of this population. METHODS: Residents in the target age group (between 18 and 64), and staff who work with the target age group, were interviewed individually to identify important questions to be asked in the assessment tool. A preliminary tool was presented to the participants in a focus group, and feedback was used to make modifications to the tool. RESULTS: Questions developed from the study addressed several unique needs of this population, including the role of technology in their well-being, the need for time with visitors, and the need for supports as they transition in to LTC. CONCLUSIONS: The needs of younger residents in LTC are unique, and through interviews with residents and staff we developed an assessment tool to better represent those needs at the time of admission.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".