Service providers’ use of harm reduction approaches in working with older adults experiencing abuse: a qualitative study
Notice bibliographique
Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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