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Enregistrement W2142902534 · doi:10.1111/j.1744-1609.2008.00110.x

Improving patient experiences of health care

2008· editorial· en· W2142902534 sur OpenAlexaboutno aff
Kate Seers

Notice bibliographique

RevueInternational Journal of Evidence-Based Healthcare · 2008
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePatient Safety and Medication Errors
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth careNursingMedicinePsychologyPolitical science

Résumé

récupéré en direct d'OpenAlex

We want to be sure that the health care we receive is as safe as possible. Patient Safety is a high profile and high priority concern across healthcare settings. The importance attributed to this topic by the World Health Organisation (WHO) is reflected in the establishment of the World Alliance for Patient Safety. This alliance aims to raise ‘awareness and political commitment to improve the safety of care and facilitates the development of patient safety policy and practice in all WHO Member States’.1 This commitment to patient safety is reflected in many countries. For example, in the UK the Department of Health set up the National Patient Safety Agency.2 In North America, both Canada3 and the USA4 have prioritised this area. The costs of patient safety failures, both in terms of economic and personal costs, are high. For example, Baker et al. in a Canadian study found the overall incidence rate of adverse events in annual hospital admissions was 7.5%.5 The WHO reported that ‘health care errors affect one in every 10 patients around the world.’6 It is thus very timely that Runciman et al. consider the epistemology of patient safety in this issue of the journal.7 They highlight the complexity of managing patient safety in a changing and diverse healthcare context, where there is often uncertainty. They stress the many ways in which things can go wrong and highlight the importance of building research capacity in this field, including qualitative research in both developed and developing countries. They also highlight the importance of individual, team and organisational level performance. Their article provides many useful pointers for future development. Also in this issue, another topic that has a major impact on patients in hospital – interventions for postoperative pain management.8 Ensuring optimum pain management after surgery is a crucial part of care. There have been many reports over several decades suggesting postoperative pain relief is not always ideal.9 In this issue, a systematic review examines the effectiveness of nursing interventions in reducing or relieving post-operative pain.8 Nursing interventions are broadly defined in this review, covering administration of analgesics as well as education, assessment of pain, use of protocols and non-pharmacological interventions. The authors accept that defining nursing intervention will be ‘local and arbitrary’ because the role and scope of nursing differed between countries. Given the size of the problem of postoperative pain management, it is rather disappointing that only nine studies could be included in the meta-analysis (with another 20 in a narrative review). Many studies had very small sample sizes and the authors rightly urge caution in interpreting the results, which had often to be based on single studies. They found there was no strong evidence to support the use of any intervention. A very clear message coming out of this review is the need for well-designed primary studies. A related resource which can help in decision making over effectiveness of analgesics in acute pain is the numbers needed to treat (NNT) table in Bandolier.10 This includes information from systematic reviews of randomised controlled trials of single dose studies in patients with moderate to severe pain. ‘Analgesic efficacy is expressed as the NNT, the number of patients who need to receive the active drug for one to achieve at least 50% relief of pain compared with placebo over a 4–6 h treatment period.’10 It is well worth consulting these tables and discussing with colleagues and patients as appropriate as you work together to try to improve acute pain management. This is one source of strong research evidence that does exist. Kate Seers, BSc(Hons) PhD RN Director, Royal College of Nursing Research Institute, University of Warwick, Coventry, UK

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)0,022
score de la tête « metaresearch » (Gemma)0,064
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: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,118

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0220,064
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0110,012
Communication savante0,0140,014
Science ouverte0,0030,027
Intégrité de la recherche0,0050,012
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

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 Opus0,176
Tête enseignante GPT0,488
Écart entre enseignants0,313 · 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
GenreÉditorial

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
Publié2008
Routes d'admission1
Résumé présentoui

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