The Case for Individualized Goal Attainment Scaling Measurement in Elder Abuse Interventions
Bibliographic record
Abstract
Research available to inform the development of effective community-based elder abuse protective response interventions is severely limited. Elder abuse intervention research is constrained by a lack of research capacity, including sensitive and responsive outcome measures that can assess change in case status over the course of intervention. Given the heterogeneous nature of elder abuse, standard scales can lack the flexibility necessary to capture the diverse range of individually relevant issues across cases. In this paper, we seek to address this gap by proposing the adaptation and use of an innovative measurement strategy-goal attainment scaling-in the context of elder protection. Goal attainment scaling is an individualized, client-centered outcome measurement approach that has the potential to address existing measurement challenges constraining progress in elder abuse intervention research.
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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 | 0.300 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".