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Improving patient experiences of health care

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

Bibliographic record

VenueInternational Journal of Evidence-Based Healthcare · 2008
Typeeditorial
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careNursingMedicinePsychologyPolitical science

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.488
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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