Interrater reliability of Algo used by non-occupational therapist members of homecare interdisciplinary teams
Bibliographic record
Abstract
PURPOSE: To determine if non-occupational therapists (non-OTs) with different job titles using Algo, a clinical algorithm for recommending bathroom modifications (e.g., bath seat) for community-dwelling elders in "straightforward" situations, will make clinically equivalent recommendations for standardized clients. METHOD: Eight non-OTs (three social workers, two physical rehabilitation therapists, two homecare aides and one auxiliary nurse) were trained on Algo and used it with six standardized clients. Bathroom adaptations recommended (one of nine options) by non-OTs were compared to assess interrater agreement using Fleiss adapted kappa. RESULTS: Estimated kappa was 0.43 [0.36; 0.49] qualified as a moderate agreement, according to Landis and Koch's arbitrary divisions, among the recommendations of non-OTs. However, clinical equivalence is reached, since safety and client needs were met when raters selected two different options (e.g., with or without a seat back). CONCLUSIONS: Non-OTs using Algo in the same simulated clinical scenarios recommend clinically equivalent bathroom adaptations, increasing the confidence regarding the interrater reliability of Algo used by non-OT members of homecare interdisciplinary teams Implications for Rehabilitation In homecare services, non-occupational therapists from different health care disciplines (e.g., homecare aides, social workers, physical rehabilitation therapists) may be asked to select assistive devices for the hygiene care of clients living at home. Algo was designed to guide non-occupational therapists in the selection of assistive devices when performed with clients in straightforward cases. This study indicates that non-occupational therapists using Algo recommend similar and acceptable bathroom adaptations to enhance client safety.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".