Content and analysis of a knowledge translation activity for an elder abuse detection tool: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) is challenging to carry out and assess. The content of a program developed to foster KT activities pertaining to the Elder Abuse Suspicion Index (EASI)©, a tool to help identify elder abuse, is described, along with reporting and analysis of some of its outcomes. METHODS: Enquiries about the use of the EASI were encouraged through completion of a structured questionnaire available on an EASI website. These were submitted by email and guided individualized responses. Descriptive data collated anonymously from the questionnaires described in aggregate corresponders' occupations, countries of work, information needs about the tool, and intent of use. The processes that generated this data were evaluated as to whether they conformed to established elements of KT. RESULTS: One hundred thirty-eight queries were received over 6 years coming from enquirers with 12 different professional backgrounds, working in 25 countries. The information sought aimed to facilitate EASI use in clinical, quality improvement, public health, research, teaching, KT, and commercial ventures. CONCLUSIONS: This activity, incorporating recognized elements of a KT undertaking, documents specific global interests in elder abuse detection. It suggests a model for researchers to gauge interest in their findings and to promote exchange around them.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".