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Enregistrement W2999090105 · doi:10.3310/hsdr08020

Implementation of interventions to reduce preventable hospital admissions for cardiovascular or respiratory conditions: an evidence map and realist synthesis

2020· article· en· W2999090105 sur OpenAlexaboutno aff
Duncan Chambers, Anna Cantrell, Andrew Booth

Notice bibliographique

RevueHealth Services and Delivery Research · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Disease Management Strategies
Établissements canadiensnon disponible
Organismes subventionnairesDepartment of Health and Social CareEvidence Synthesis ProgrammeHealth Services and Delivery Research ProgrammeNational Institute for Health and Care Research
Mots-clésPsychological interventionMedicineContext (archaeology)Intervention (counseling)Systematic reviewData extractionGrey literatureMEDLINENursing

Résumé

récupéré en direct d'OpenAlex

Background In 2012, a series of systematic reviews summarised the evidence regarding interventions to reduce preventable hospital admissions. Although intervention effects were dependent on context, the reviews revealed a consistent picture of reduction across different interventions targeting cardiovascular and respiratory conditions. The research reported here sought to provide an in-depth understanding of how interventions that have been shown to reduce admissions for these conditions may work, with a view to supporting their effective implementation in practice. Objectives To map the available evidence on interventions used in the UK NHS to reduce preventable admissions for cardiovascular and respiratory conditions and to conduct a realist synthesis of implementation evidence related to these interventions. Methods For the mapping review, six databases were searched for studies published between 2010 and October 2017. Studies were included if they were conducted in the UK, the USA, Canada, Australia or New Zealand; recruited adults with a cardiovascular or respiratory condition; and evaluated or described an intervention that could reduce preventable admissions or re-admissions. A descriptive summary of key characteristics of the included studies was produced. The studies included in the mapping review helped to inform the sampling frame for the subsequent realist synthesis. The wider evidence base was also engaged through supplementary searching. Data extraction forms were developed using appropriate frameworks (an implementation framework, an intervention template and a realist logic template). Following identification of initial programme theories (from the theoretical literature, empirical studies and insights from the patient and public involvement group), the review team extracted data into evidence tables. Programme theories were examined against the individual intervention types and collectively as a set. The resultant hypotheses functioned as synthesised statements around which an explanatory narrative referenced to the underpinning evidence base was developed. Additional searches for mid-range and overarching theories were carried out using Google Scholar (Google Inc., Mountain View, CA, USA). Results A total of 569 publications were included in the mapping review. The largest group originated from the USA. The included studies from the UK showed a similar distribution to that of the map as a whole, but there was evidence of some country-specific features, such as the prominence of studies of telehealth. In the realist synthesis, it was found that interventions with strong evidence of effectiveness overall had not necessarily demonstrated effectiveness in UK settings. This could be a barrier to using these interventions in the NHS. Facilitation of the implementation of interventions was often not reported or inadequately reported. Many of the interventions were diverse in the ways in which they were delivered. There was also considerable overlap in the content of interventions. The role of specialist nurses was highlighted in several studies. The five programme theories identified were supported to varying degrees by empirical literature, but all provided valuable insights. Limitations The research was conducted by a small team; time and resources limited the team’s ability to consult with a full range of stakeholders. Conclusions Overall, implementation appears to be favoured by support for self-management by patients and their families/carers, support for services that signpost patients to consider alternatives to seeing their general practitioner when appropriate, recognition of possible reasons why patients seek admission, support for health-care professionals to diagnose and refer patients appropriately and support for workforce roles that promote continuity of care and co-ordination between services. Future work Research should focus on understanding discrepancies between national and international evidence and the transferability of findings between different contexts; the design and evaluation of implementation strategies informed by theories about how the intervention being implemented might work; and qualitative research on decision-making around hospital referrals and admissions. Funding The National Institute for Health Research Health Services and Delivery Research programme.

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)0,134
score de la tête « metaresearch » (Gemma)0,297
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,134
Score d'incertitude au seuil0,708

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1340,297
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0090,011
Bibliométrie0,0470,029
Études des sciences et des technologies0,0020,003
Communication savante0,0150,012
Science ouverte0,0050,009
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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 Opus0,313
Tête enseignante GPT0,520
Écart entre enseignants0,207 · 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'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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

En bref

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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