Development and preliminary assessment of the measurement properties of the Seating Identification Tool (SIT)1
Bibliographic record
Abstract
OBJECTIVE: To present and discuss the development and measurement properties of the Seating Identification Tool (SIT), a screening tool designed to identify the need for formal seating and wheelchair intervention among institutionalized elderly. Specifically, investigation of the inter-rater and test-retest reliability, sensitivity, specificity, the positive and negative predictive values of the SIT was conducted. DESIGN: A two-week retest design. SETTING: A long-term care facility in London, Ontario, Canada. SUBJECTS: Forty-two randomly selected residents with an average age of 83 years who had a disability and required the use of a wheelchair as their main mode of mobility. INTERVENTION: Two health care assistants from a long-term facility collected data using the SIT. One rater assessed all subjects two weeks later to evaluate test-retest reliability. Diagnostic properties (validity) were determined by having all subjects assessed by a seating therapist. MAIN MEASUREMENT: The SIT and formal evaluation by a therapist experienced in seating. RESULTS: The ICC for both test-retest and inter-rater reliability was 0.83. A cut-off score of 2 maximized the sensitivity (100%) and specificity (64% and 57% for raters 1 and 2 respectively) and the area under the receiver operating characteristics curve (0.855 and 0.862 for raters 1 and 2). The positive and negative predictive values ranged from 82 to 100%. CONCLUSION: The SIT is a quick, easy to use, reliable and valid screening tool that can be used to facilitate clinical referral for formal intervention. Other potential uses include population-based surveys to estimate the need for including seating intervention in strategic planning for the institutionalized elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| 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 | 0.001 |
| 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".