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Enregistrement W4396235325 · doi:10.1093/ijpp/riae013.029

A-I-D for Cascades: Designing a theory-informed intervention for addressing prescribing cascades in primary care

2024· article· en· W4396235325 sur OpenAlexaff
Lisa McCarthy, Barbara Farrell, Colleen Metge, Lianne Jeffs, Sameera Toenjes, M. Christine Rodriguez

Notice bibliographique

RevueInternational Journal of Pharmacy Practice · 2024
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Policy Implementation Science
Établissements canadiensLunenfeld-Tanenbaum Research InstituteUniversity of ManitobaUniversity of OttawaBruyèreWomen's College HospitalTrillium Health CentreUniversity of TorontoUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésMedicinePrimary careIntervention (counseling)NursingFamily medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Prescribing cascades, which occur when a medication is used to treat the side effect of another medication, are important contributors to polypharmacy. There is an absence of studies that evaluate the implementation or impact of existing interventions to address prescribing cascades in practice. Aim To design theory-informed options for interventions to address prescribing cascades within interprofessional primary care teams. Methods The Behaviour Change Wheel (BCW) framework, and its eight steps, were applied to guide intervention development by the research team. Three target behaviours were drafted and prioritised for intervention development based on data collected as part of two qualitative studies exploring why and how cascades occur across practice settings.[1,2] A target behaviour was selected and the COM-B (capability, opportunity, motivation-behaviour) model was then applied to identify the relevant factors for interprofessional primary care teams. The BCW was used to determine the relevant intervention types and policy options for the behaviour. Next, corresponding behaviour change techniques (BCTs) were identified and intervention options drafted. Prioritisation of behaviours and intervention examples were guided by the APEASE criteria (Affordability, Practicability, Effectiveness/cost-effectiveness, Acceptability, Side-effects/safety, Equity). Results The three target behaviours involved supporting: 1) healthcare providers to ask about, investigate and manage cascades (often through deprescribing), 2) the public to ask about prescribing cascades, and 3) the public to share medication histories and experiences with healthcare providers. The team selected the healthcare provider behaviour, called A-I-D (ask, investigate, deprescribe), for intervention development. Psychological capability and physical opportunity were determined to be the most relevant COM-B components, corresponding to education, training, environmental restructuring, and enablement intervention types and the guidelines, communications and marketing, and service provision policy options within the BCW model. Ultimately, 10 intervention options comprised of BCTs were developed by the team, which are ready for further prioritisation by stakeholders. These can be grouped into three categories: provision of educational content or materials for use by clinicians, provision of consultation or training to support clinicians, and knowledge mobilisation strategies. Through the process, the team identified that development of a practice guidance tool, which assists healthcare providers to investigate and manage prescribing cascades, is needed to support further intervention development. Conclusion The BCW framework guided the design of intervention options that will support primary care clinicians practising in interprofessional teams to address prescribing cascades. A limitation of this work is that applying the BCW framework required several judgements by the team, comprised of scientists, pharmacists, and nurses but not a general practitioner physician. Many but not all have practised with primary care interprofessional teams. When identifying interventions for future consultation, it was determined that the development of a practice guidance tool (i.e., which assists with identifying, investigating, and managing prescribing cascades) underpinned all the proposed interventions for addressing prescribing cascades in practice. Further research is needed to determine what primary care clinicians will need in this practice guidance tool and how it will be used in practice, to support its development. References 1. Farrell BJ, Jeffs L, Irving H et al. Patient and provider perspectives on the development and resolution of prescribing cascades: a qualitative study. BMC Geriatr 2020;20:368. 2. Farrell B, Galley E, Jeffs L, Howell P, McCarthy LM. “Kind of blurry”: Deciphering clues to prevent, investigate and manage prescribing cascades. PLoS ONE 2022;17(8): e0272418.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,013
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,859
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,013
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,003
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,551
Tête enseignante GPT0,692
Écart entre enseignants0,141 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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