Implementing a Systematic Screening Procedure for Older Adult Mistreatment Within Individual Clinical Supervision: Is It Feasible?
Bibliographic record
Abstract
Home care professionals are well positioned to witness or prevent older adult mistreatment in the community. Screening efforts are important because most victims will not easily come forth. Two Canadian local community service centers implemented a systematic screening procedure within preexisting individual clinical supervision sessions to support social workers and improve detection of mistreatment. The aim of this pilot project was to assess fidelity, acceptability, and feasibility of the new procedure. Qualitative data was collected using individual interviews with two clinical supervisors, one focus group with eight social workers and content transcribed from 15 supervision sessions. It was estimated that 400 clients were screened for older adult mistreatment using this new procedure. Results showed the procedure was judged acceptable because it sensitized social workers to risk factors, gave them time to reflect upon and discuss probable cases with their clinical supervisor. Nonetheless, participants did not use the designated statistical code in the new procedure to document mistreatment situations. Feasibility was mainly challenged by the fact that screening for older adult mistreatment competes with other organizational priorities. Future initiatives must develop strategies to counteract those barriers.
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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 | 0.073 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".