Design and development of the home healthcare worker observation tool
Bibliographic record
Abstract
Background: The rapidly growing number of home healthcare workers (HHCW) are exposed to unique occupational hazards within each patient home. This article describes the development of an observation tool to document occupational hazards HHCWs encounter. Methods: Tool development followed three steps: determining content domain, content validity, and inter-rater agreement. Results: Expert feedback guided the revision of content domain to 636 items. Scale level content validity index (S-CVI) was 0.90. Inter-rater agreement tests resulted in percent agreement and accuracy mean of 89.5% and frequency variables resulted in standard deviations from 0 to 8.62. Conclusions: The observation tool encompasses the diverse range of occupational hazards HHCWs encounter; inter-rater percent agreement and overall accuracy scores were acceptable. Future pilot testing of this tool among broader raters and populations is recommended to characterize its usability, internal consistency, and reliability to assess HHCW occupational hazards.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".