Notice bibliographique
Résumé
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 distillée sur la base complète
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».