Service providers’ use of harm reduction approaches in working with older adults experiencing abuse: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Although abuse experienced by older adults is common and expected to increase, disclosure, reporting and interventions to prevent or mitigate abuse remain sub-optimal. Incorporating principles of harm reduction into service provision has been advocated as a strategy that may improve outcomes for this population. This paper explores whether and how these principles of harm reduction were employed by professionals who provide services to older adults experiencing abuse. METHODS: Thematic analysis of qualitative interviews with 23 professionals providing services to older adults experiencing abuse across three Western provinces of Canada was conducted. Key principles of harm reduction (humanism, incrementalism, individualism, pragmatism, autonomy, and accountability without termination) were used as a framework for organizing the themes. RESULTS: Our analysis illustrated a clear congruence between each of the six harm reduction principles and the approaches reflected in the narratives of professionals who provided services to this population, although these were not explicitly articulated as harm reduction by participants. Each of the harm reduction principles was evident in service providers' description of their professional practice with abused older adults, although some principles were emphasized differentially at different phases of the disclosure and intervention process. Enactment of a humanistic approach formed the basis of the therapeutic client-provider relationships with abused older adults, with incremental, individual, and pragmatic principles also apparent in the discourse of participants. While respect for the older adult's autonomy figured prominently in the data, concerns about the welfare of the older adults with questionable capacity were expressed when they did not engage with services or chose to return to a high-risk environment. Accountability without termination of the client-provider relationship was reflected in continuation of support regardless of the decisions made by the older adult experiencing abuse. CONCLUSIONS: Harm reduction approaches are evident in service providers' accounts of working with older adults experiencing abuse. While further refinement of the operational definitions of harm reduction principles specific to their application with older adults is still required, this harm reduction framework aligns well with both the ethical imperatives and the practical realities of supporting older adults experiencing abuse.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".