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Enregistrement W1967798898 · doi:10.1111/j.1744-1609.2012.00278.x

Context matters … more than ever

2012· letter· en· W1967798898 sur OpenAlexaff
Tazim Virani

Notice bibliographique

RevueInternational Journal of Evidence-Based Healthcare · 2012
Typeletter
Langueen
DomaineHealth Professions
ThématiqueHealth Sciences Research and Education
Établissements canadiensRegistered Nurses' Association of Ontario
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Computer scienceHistoryArchaeology

Résumé

récupéré en direct d'OpenAlex

Practitioners frequently complain of barriers to implementing practice changes such as lack of buy-in, lack of time, heavy workload and insufficient resources; and yet interventions to improve the uptake of evidence-based practices in research studies and practice settings appear to ignore these important variables in the design of the knowledge uptake interventions. The Josefsson et al. article in this issue1 identified practical and structural barriers to the use of evidence-based practice, while Toh et al.2 identified shortage of staff as a factor in job satisfaction among nurses. Ignoring these organisational barriers is futile – wheels are spun and the gap between research and practice continues to remain constant. When implementing clinical practice changes in organised care (e.g. hospitals, nursing homes, home care agencies, primary care practices), individual practitioners are not always able to change their practices without organisational sanctions and support.3,4 To better understand and support clinical practice change in organised care, I argue that researchers and practitioners have failed to leverage the availability of organisational theory/theories to understand, design and study interventions that can address the recurrent organisational or context barriers. Implementing evidence-based practices is highly context dependent.3 Rycroft-Malone et al.5 included context as one of the three main variables in the widely cited, although not adequately tested6 Promoting Action on Research Implementation in Health Services (PaRiHS) framework to support the uptake of evidence-based practices; the other two variables are being evidence and facilitation. They proposed that the successful implementation of practice change is influenced by the nature of the context such as the prevailing culture, leadership roles, availability of resources and the fit of the practice with organisational structures and procedures. Similarly, Grol and Wensing7 identified organisational context as a key category of barriers for drivers of change in clinical practice in their review of barriers to practice change. They identified organisation of care processes, staff, capacities, resources and structures as examples of organisation context barriers. What is disturbing is that the calls for research to better understand the uptake of clinical evidence at the organisational level or context8–10 has had little response. A recent repeat systematic review on organisational infrastructure to promote the uptake of evidence-based practice concluded that there is a dearth of research studies evaluating organisational infrastructure interventions.11 In fact, the reviewers were able to include only one such study based on their inclusion criteria. The previous systematic review was empty.12 Organisational infrastructure was defined as the framework within which care is delivered and supported. Infrastructure can be thought of as the policies, people, processes and tools that support care provision and include examples such as supports for capacity development, routines in providing care, policies on such aspects as scope of practice and service delivery models, protocols, clinical pathways, standing orders, information and communication systems, mechanisms to discuss care issues such as clinical rounds, materials and equipment, space and so on. Addressing organisational level barriers in designing practice change interventions is needed considerably. Using organisational theory to frame the design of interventions will anchor practice and research and promote a sound knowledge base for the uptake of evidence-based practice change. One such organisation theory is organisational learning theory13 which can be useful in designing and studying the interventions for implementing practice change and uptake of evidence-based practices. Organisational learning is the searching, acquiring, storing, retrieving and use of knowledge to meet organisational objectives.13 Each of these organisational learning processes is influenced by organisational characteristics (as was found in Fareed's14 study on hospital size). For example, the process of storing knowledge is dependent on the availability of carriers of knowledge such as policies, procedures, training modules, stories as a form of cultural carrier, etc.15 The ability of the organisation to store knowledge and initiate its use when required is referred to as organisational memory.15 Successful implementation of practice change is, therefore, a function of the organisational infrastructure and supports to facilitate successful organisational learning and organisational memory. Designing interventions with such theories as organisational learning confronts the well-established barriers to practice change such as lack of time and resources16 and misfit with organisational priorities.17 In order to call attention to organisational factors that support or hinder practice change, it is imperative that managers of organised healthcare have a greater knowledge and understanding of evidence-based practice and organisational change. It is not only the purview of clinicians who need to engage in evidence-based practice but also managers who need to pave the path for effective conditions within which such practices can flourish. Specifically, leveraging direction from organisational learning, it is possible to support the institutionalisation of practices through processes such as appropriate formats for storing, retrieval and use. For example, storing practice knowledge requires modification of existing structures such as admission and assessment processes or a different routine in the discharge protocol. Managers can be helpful in identifying and enabling the organisational processes to making these changes in the most efficient way possible. Using clinician time to navigate the complexity of organisations is not an appropriate use of their expertise and time. Additionally, managers and leaders in organisations need to be vigilant in reinforcing practice changes through strategies such as audit and feedback, quality improvement cycles, practice prompts and reminders.3 I conclude with two calls for action. First, interventions to address barriers to practice change must go beyond the individual and address context-related barriers. It is no longer appropriate to provide education and training to staff as the only mechanism to bring about clinical practice change. Continuing education is an important but not sufficient intervention. Assessing local contextual barriers and addressing these upfront in the design of interventions is critical. Tailoring the interventions to the local context has been recommended previously3,7,18 and needs to continue to be a guiding principle. The goal should be to search for ways to have knowledge ‘stick’ in organisational work flow and processes while making it easy for practitioners to use evidence-based practice through the use of cues and reminders that are engineered in the organisational structures and processes.3 The institutionalisation of a safety checklist in surgical care, for example, is a key organisational tool that has been shown to integrate well in the workflow and design of operating room culture and set up while significantly reducing post-surgical morbidity and mortality.19 Second, as has been called for previously,8–10 I lend my voice to the call for the use of organisational theory in supporting evidence-based practice, in organised care settings and to frame implementation research at the organisation level. It is time to create balanced attention beyond the individual healthcare provider and focus on the context.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,046
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,053

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,046
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0090,010
Communication savante0,0090,014
Science ouverte0,0020,006
Intégrité de la recherche0,0460,079
Charge utile insuffisante (le modèle a refusé de juger)0,0140,006

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 Opus0,465
Tête enseignante GPT0,553
Écart entre enseignants0,088 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2012
Routes d'admission1
Résumé présentnon

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