Development and psychometric testing of the 5Senses screening tool for long-term care: a study protocol
Bibliographic record
Abstract
INTRODUCTION: As adults age, their senses tend to decline and a large portion of those most affected by sensory decline reside in long-term care. At present, the creation of a sense-sensitive environment in long-term care is a difficult task as there is minimal evidence or tools available to guide this process. The 5Senses screening tool was developed to measure the sense-sensitivity of a particular environment, with a focus on long-term care. The purpose of this paper is to describe a study protocol to assess the psychometric properties of the newly developed 5Senses screening tool. METHODS AND ANALYSIS: We will conduct a psychometric evaluation of the 5Senses screening tool in long-term care based on the Standards for Educational and Psychological Testing Framework. In phase I, we will seek input from international content experts (n=20) to assess the content validity of all sections of the tool. In phase II, we will invite auditors (n=3-9), residents (n=3-9) and staff (n=3-9) to partake in think-aloud sessions to assess response process validity. In phase III, we will conduct field testing of the revised 5Senses screening tool with auditors (n=100), residents (n=100) and staff (n=100) to evaluate additional measures including acceptability, inter-rater reliability, internal structure validity and internal consistency reliability, where possible. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Ottawa Research Ethics Board. Findings will be disseminated through a peer-reviewed manuscript, through a dedicated website, through presentations in long-term care communities and through presentations at research conferences.
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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 | 0.059 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".