UNDERSTANDING FUNCTIONS OF CLINICAL SUPERVISION IN THE IDENTIFICATION OF OLDER ADULT MISTREATMENT
Bibliographic record
Abstract
For social workers, identification of older adult mistreatment is a complex social process that requires making decisions based on available information while following organizational procedures. Clinical supervision is a promising avenue to support social workers in their decision-making process since discussing cases with colleagues or a superior has been recognized as a facilitator. Up to date, few studies explored real-life clinical supervision in social work. The aim of this study was to explore the type of information discussed and supervision strategies used by clinical supervisors to support identification of older adult mistreatment by social workers. Qualitative data collection included the content of fifteen clinical supervision meetings, individual interviews with two clinical supervisors, and a focus group with eight social workers. All data were transcribed and analyzed using the Miles, Huberman and Saldaña (2014) analytical method. Results demonstrated three main functions of clinical supervision to support identification of older adult mistreatment: 1) clarifying case features that are relevant for the decision-making process; 2) choosing individualized intervention options; and 3) managing difficulties encountered by social workers. Strategies used by supervisors included a questions-and-answers approach to guide data collection; reinforcing/praising; challenging the point of view of the social worker and collaborating in the development of the individualized intervention plan. Social workers considered that identification is intertwined with the responsibility to intervene in the context of older adult mistreatment. This study is a first step in the elaboration of a model illustrating the functions of clinical supervision in the fight against older adult mistreatment.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".