PROTOCOL: Psychometric properties of instruments for measuring elder abuse and neglect in community and institutional settings: A systematic review
Bibliographic record
Abstract
The psychometric properties of elder abuse measurement instruments have not been well-studied. Poor psychometric properties of elder abuse measurement instruments may contribute to the inconsistency of elder abuse prevalence estimates and uncertainty about the magnitude of the problem at the national, regional, and global levels.The present review will utilise the COSMIN taxonomy on the quality of outcome measures to identify and review the instruments used in measuring elder abuse, assess the instrument's measurement properties, and identify the definitions of elder abuse and abuse subtypes measured by the instrument.Searches will be conducted in the following online databases: Ageline, ASSIA, CINAHL, CNKI, EMBASE, Google Scholar, LILACS, Proquest Dissertation & Theses Global, PsycINFO, PubMed, SciELO, Scopus, Sociological Abstract and WHO Index Medicus. Relevant studies will also be identified by searching the grey literature from several resources such as OpenAIRE, BASE, OISter and Age Concern NZPotential studies by searching the references of related reviews. We will contact experts who have conducted similar work or are currently conducting ongoing studies. Enquiries will also be sent to the relevant authors if any important data is missing, incomplete or unclear.All quantitative, qualitative (that address face and content validity), and mixed-method empirical studies published in peer-reviewed journals or the grey literature will be included in this review. Studies will be included if they are primary studies that (1) evaluate one or more psychometric properties; (2) contain information on instrument development, or (3) perform content validity of the instruments designed to measure elder abuse in the community or institutional settings. Studies should describe at least one of the psychometric properties, such as reliability, validity and responsiveness. Study participants represent the population of interest, including males and females aged 60 or older in community or institutional settings (i.e., nursing homes, long-term care facilities, assisted living, residential care institutions, and residential facilities).Screening of titles, abstracts, and full texts of the selected studies will be evaluated based on the preset inclusion criteria by two reviewers. Two reviewers will be assessing the quality appraisal of each study using the COSMIN Risk of Bias checklist and the overall quality of evidence of each psychometric property of the instrument against the updated criteria of good measurement properties. Any dispute between the two reviewers will be resolved through discussions or consensus with a third reviewer. The overall quality of the measurement instrument will be graded using a modified GRADE approach. Data extraction will be performed using the data extraction forms adapted from the COSMIN Guideline for Systematic Reviews of Outcome Measurement Instruments. The information includes the characteristic of included instruments (name, adaptation, language used, translation and country of origin), characteristics of the tested population, psychometric properties listed in the COSMIN criteria, including details on the instrument development, content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, responsiveness and interoperability. We will perform a meta-analysis to pool psychometric properties parameters (where possible) or summarise qualitatively.
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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 | 0.000 |
| 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 | 0.001 |
| 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".