Development of Algo, a clinical algorithm for non–occupational therapists selecting bathing equipment
Bibliographic record
Abstract
BACKGROUND: In Quebec, occupational therapy guidelines allow non-occupational therapists, such as home health aides, to select bathing equipment for "straightforward" cases of clients living at home as long as the aides use a decision-making tool. PURPOSE: Our aim was to develop a tool that met the common needs of Quebec's health and social services centres (HSSCs), which involve home health aides in selecting bathing equipment for home-dwelling clients. METHOD: We followed an ongoing iterative process involving a literature review as well as (a) a synthesis of 40 in-house tools, (b) feedback from 10 occupational therapists (two questionnaires and one focus group), (c) pretests, and (d) translation. FINDINGS: Algo is a clinical algorithm constituting a visual map of the logical steps to follow when selecting bathing equipment for straightforward cases. Algo is a series of yes/no questions dealing with occupation, person, and environment. IMPLICATIONS: Algo, rooted in evidence and regulatory board guidelines, is available to HSSCs involving non-occupational therapists in selecting bathing equipment.
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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 | 0.043 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".