Can home health aids using the clinical algorithm Algo choose the right bath seat for clients having a straightforward problem?
Bibliographic record
Abstract
OBJECTIVE: To determine if Algo, a clinical algorithm to select bathing equipment for 'straightforward' cases, guides home health aides in selecting the appropriate bath seat. DESIGN: Criterion validity study. SETTING: Community home care. SUBJECTS: Eight home health aides used Algo with community-dwelling older adults having a straightforward problem. MAIN MEASURES: Their bath-seat recommendations were compared with those proposed by an occupational therapist (OT), which were considered as the gold standard. In order to determine a clinically acceptable threshold of agreement between the recommendations, a subgroup of community-dwelling elderly people was assessed a third time by another OT. RESULTS: Half of the clients (74/143) for whom bathroom assessments were requested qualified as potentially straightforward cases after triage and were visited at home by a home health aide using Algo. In 84% of cases (95% confidence interval (CI) = [75, 93]), the non-OTs using Algo identified a seat that would enable these older adults to bathe according to their preferences, abilities and environment, as confirmed by the gold standard OT. Moreover, this appropriateness rate did not statistically differ from that obtained when comparing another OT to the gold standard. CONCLUSION: Algo guides non-OTs toward a bath seat that meets the needs of community-dwelling older adults in the majority of cases.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".