“Establishing the criterion validity of the interRAI Check-Up Self-Report instrument”
Bibliographic record
Abstract
BACKGROUND: Low and middle-income countries have growing older populations and could benefit from the use of multi-domain geriatric assessments in overcoming the challenge of providing quality health services to older persons. This paper reports on the outcomes of a study carried out in Cape Town, South Africa on the validity of the interRAI Check-Up Self-Report instrument, a multi-domain assessment instrument designed to screen older persons in primary health settings. This is the first criterion validity study of the instrument. The instrument is designed to identify specific health problems and needs, including psychosocial or cognition problems and issues related to functional decline. The interRAI Check-Up Self-Report is designed to be compatible with the clinician administered instruments in the interRAI suite of assessments, but the validity of the instrument against clinician ratings has not yet been established. We therefore sought to establish whether community health workers, rather than trained healthcare professionals could reliably administer the self-report instrument to older persons. METHODS: We evaluated the criterion validity of the self-report instrument through comparison to assessments completed by a clinician assessor. A total of 112 participants, aged 60 or older were recruited from 7 seniors clubs in Khayelitsha, Cape Town. Each participant was assessed by one of two previously untrained, non-healthcare personnel using the Check-Up Self-report version and again by a trained assessor using the clinician version of the interRAI Check-Up within 48 h. Our analyses focused on the degree of agreement between the self-reported and clinician-rated versions of the Check-Up based on the simple or weighted kappa values for the two types of ratings. Binary variables used simple kappas, and ordinal variables with three or more levels were examined using weighted kappas with Fleiss-Cohen weights. RESULTS: Based on Cohen's Kappa values, we were able to establish that high levels of agreement existed between clinical assessors and lay interviewers, indicating that the instrument can be validly administered by community health workers without formal healthcare training. 13% of items had kappa values ranging between 0.10 and 0.39; 51% of items had kappa values between 0.4 and 0.69; and 36% of items had values of between 0.70 and 1.00. CONCLUSION: Our findings indicate that there is potential for the Check-Up Self-Report instrument to be implemented in under-resourced health systems such as South Africa's.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".