Journal of evaluation in clinical practice最新文献

Background: Preventing errors associated with medication administration is achievable through nurses' adherence to correct principles.

Objective: This study aims to investigate the relationship between nurses' adherence to medication administration principles and their attitudes toward patient safety.

Method: This study adopts a descriptive and relationship-seeking research design. A total of 310 nurses who met the inclusion criteria participated in the study. Data collection tools included the "Nurse Information Form," "Medication Administration Principles Adherence Form," and "Patient Safety Attitude Scale".

Results: Among the participating nurses, 32.9% (n = 102) reported experiencing a medication administration error, and 77.7% (n = 241) witnessed such an error. The study revealed a statistically significant positive relationship between nurses' adherence to medication administration principles and their attitudes toward patient safety (p < 0.001).

Conclusions: This study underscores the importance of nurses' adherence to medication administration principles in ensuring patient safety.

Background: Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity.

Objectives: this study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and identify important variables in constructing the model through permutation importance.

Methods: By using the recursive feature elimination, cross-validated technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining RandomForest, XGBoost, and LightGBM algorithms was constructed. The predictive performance and permutation importance of each variable were evaluated.

Results: The variables selected to construct the model were age, sex, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and sex (0.034).

Conclusion: The performance of the model for predicting OA was relatively good. If this model is continuously used and updated, it could be used to predict OA diagnosis, and the predictive performance of the OA model may be further improved.

Objectives: The study aimed to (1) determine if a variety of workplace and supervision factors predict clinical supervision effectiveness; and (2) establish if clinical supervision effectiveness predicts burnout, amongst a variety of mental health staff (medical, allied health, and nursing staff).

Design: The study adopted a multicentre cross-sectional online survey design.

Methods: Participants included 204 mental health staff (89 allied health staff, 81 nursing staff, and 34 medical staff). The Manchester Clinical Supervision Scale (MCSS-26) was used to measure clinical supervision effectiveness, and the Maslach Burnout Inventory (MBI-HSS) was used to measure burnout. Linear regression analyses and multiple regression analyses were conducted.

Results: The main findings suggested that supervision frequency, supervision duration, choice of supervisor, workplace setting, and supervisee profession, were all significant predictors of clinical supervision effectiveness. Additionally, clinical supervision effectiveness was a significant negative predictor of burnout.

Conclusions: Workplace and supervision factors should be considered in supervision practice across professional groups. Policies need to promote effective clinical supervision practice.

Introduction: One of the most common assessment tools for examining the competencies of health professionals is the Objective Structured Clinical Examination (OSCE).

Methods: In this study, an Educational Effects of OSCE Questionnaire (EEOQ) was developed and validated in seven steps: (1) Reviewing the literature, (2) Holding focus groups, (3) Synthesizing the results of literature review and focus groups, (4) Developing items, (5) Evaluating content validity, (6) Conducting cognitive interviews, and (7) Implementing a pilot test.

Results: The analysis of the literature review and focus groups revealed that the educational effects of OSCE are influenced by factors related to the test, the examiner, the peers, and the student. Initially, 22 items were developed, but one item was excluded based on the results of the I-CVR and I-CVI indices. In the Exploratory Factor Analysis, the KMO index was computed to be 0.85, and the significance of Bartlett's test of sphericity was confirmed (p < 0.001). The Total Variance Explained table showed that the educational effects of OSCE were grouped into four factors: content, structure, reflection and review, and feedback along with OSCE.

Conclusions: This study found good evidence of validity and reliability for a questionnaire measuring the educational effects of OSCEs. It was discovered that OSCE can have different effects on student learning processes.

Rationale: Loss to follow-up of participants can compromise the statistical validity of randomised trials. Moreover, it can have financial consequences for trial teams and funders. This study explores the Occupational Therapist Intervention Study (OTIS) where, despite a withdrawal rate of less than 10%, the trial team incurred opportunity costs related to participants who were initially recruited but subsequently decided to withdraw from the trial.

Aims and objectives: To estimate the cost of participant losses to follow-up in the OTIS trial and thus introduce a costing framework to research teams on how they could estimate the opportunity costs of participant withdrawal from their randomised trials.

Methods: The participants lost to follow-up are differentiated by (1) the time point at which they were lost to follow-up; (2) the treatment group they were allocated to; (3) their response patters to follow-up questionnaires; these elements were considered to identify the relevant types of attrition. Protocol-driven costs of trial materials, including administration, print, and shipping, were gathered. We calculated unit costs for each type of attrition by multiplying protocol-driven and intervention costs with the relevant number of participants. Summing up unit costs by type of loss to follow-up yields aggregate figures, enabling the estimation of aggregate and average opportunity costs of attrition.

Results: The average cost per participant loss to follow-up in the OTIS trial is £98.41. The aggregate cost of participant loss to follow-up is £10,234.90 from the economic perspective of the trial team. Therefore, 1.42% of the allocated funding has been misallocated because of participant loss to follow-up.

Conclusion: Despite the low attrition rate of the OTIS trial, loss to follow-up has still generated considerable opportunity costs. It is recommended that decision makers focus on identifying strategies which could improve participant retention in randomised trials to optimise their budget.

Context: From the results of the 2020 Flemish survey looking into psychiatric patients' views of the in-patient care they have received, it appeared that hospital communication is experienced as not sufficiently patient-centered. In communication research, the quality of written patient materials in physical healthcare has been scrutinized and suggestions for the enhancement of their patient-centeredness, comprehensibility, and actionability have been made. Yet, a similar research interest in the quality of health communication in mental healthcare has failed to materialize.

Objective and design: Against a definition of patient-centeredness in mental healthcare, this study investigated the quality of 30 psychiatric hospital brochures from a triangulated linguistic and content perspective, using readability formulas as well as the CCI and PEMAT-P instruments.

Results: It appeared that none of the brochures are sufficiently patient-centered, as they fail to take appropriate and full account of mental health patients' specific concerns and difficulties.

Discussion: The lack of quality brochures hampers patients' understanding of their mental health condition and participation in their recovery process, especially when they have a low (mental) health literacy.

Conclusion: Together, hospitals and patient organizations, should remedy this situation and thus fortify the public's trust in the evidence-based and high-quality patient-centered care psychiatric hospitals offer.

Background: Medical diagnosis plays a critical role in our daily lives. Every day, over 10 billion cases of both mental and physical health disorders are diagnosed and reported worldwide. To diagnose these disorders, medical practitioners and health professionals employ various assessment tools. However, these tools often face scrutiny due to their complexity, prompting researchers to increase their experimental parameters to provide accurate justifications. Additionally, it is essential for professionals to properly justify, interpret, and analyse the results from these prediction tools.

Methods: This research paper explores the use of artificial intelligence and advanced analytics in developing Clinical Decision Support Systems (CDSS). These systems are capable of diagnosing and detecting patterns of various medical disorders. Various machine learning algorithms contribute to building these assessment tools, with the Network Pattern Recognition (NEPAR) algorithm being the first to aid in developing CDSS. Over time, researchers have recognised the value of machine learning-based prediction models in successfully justifying medical diagnoses.

Results: The proposed CDSS models have demonstrated the ability to diagnose mental disorders with an accuracy of up to 89% using only 28 questions, without requiring human input. For physical health issues, additional parameters are used to enhance the accuracy of CDSS models.

Conclusions: Consequently, medical professionals are increasingly relying on these machine learning-based CDSS models, utilising these tools to improve medical diagnosis and assist in decision-making. The different cross-validation values are considered to remove the data biasness.

Objective: To explore the clinical effect of a nurse-centred multidisciplinary collaborative hospice care model in patients with advanced malignant tumours.

Methods: A total of 30 patients with advanced malignant tumours were hospitalised and randomly divided into a study group and a control group, each consisting of 15 cases. The study group received nurse-led multidisciplinary collaborative hospice care, whereas the control group underwent high-quality nursing intervention. Variables such as the self-rating anxiety scale (SAS) score, self-rating depression scale (SDS) score, quality of life scale (EORTC QLQ-C30) score, patient happiness, and nursing satisfaction were compared between the two groups.

Results: Post-intervention, the SAS and SDS scores in the study group were lower than those in the control group (p < 0.01). The overall quality of life score of the study group was higher than that of the control group (p < 0.01). The Memorial University of Newfoundland Scale of Happiness scores in the study group also surpassed those of the control group (p < 0.01). Additionally, nursing satisfaction in the study group exceeded that of the control group (p = 0.027).

Conclusion: The nurse-led multidisciplinary collaborative hospice care model substantially alleviated negative emotions among patients, effectively improved their quality of life and happiness, and garnered positive evaluations of nursing satisfaction.

Introduction: Evidence from the literature suggests that social determinants of health (SDOH) account for 80% of health outcomes and are associated with hospital readmissions. Readmissions negatively impact the quality of life of patients and increase healthcare costs. The Agency for Healthcare Research and Quality reports that there were 3.8 million 30-day adult hospital readmissions in 2018 with an average readmission cost of $15,000.

Methods: A literature search in CINAHL, PubMed, ClinicalKey, Google Scholar, and Medline databases using the keywords "SDOH", "social determinants of health", "healthcare disparities", "patient discharge", "readmission", "interdisciplinary", and "multidisciplinary" yielded 287 articles. After title and abstract screening and a full-text review of 90 articles, five articles were selected for critical appraisal. We used the critical appraisal skills programme (CASP) and Appraisal of Guidelines for Research & Evaluation II (AGREE II) tools for the selected articles' appraisal process.

Results: Ten high-quality strong evidence recommendations emerged from the evidence review process. These recommendations were bundled together to create an evidence-based methodology to decrease 30-day readmissions.

Conclusion: The findings provide strong evidence of the effectiveness of a bundled discharge approach in reducing hospital readmissions and improving patient outcomes.

Rationale: Exercise capacity declines with age. However, the effect of common geriatric symptoms, that are related to physical performance, on exercise capacity is unclear.

Aims: The study aimed to determine the impacts of sarcopenia, frailty, balance, and depression on both overall and abnormal exercise capacity.

Methods: One hundred and nineteen community-dwelling older adults over 65 years of age were included in the cross-sectional study. Sarcopenia and frailty status were determined according to the "European Working Group on Sarcopenia in Older People2" and "Fried frailty criteria", respectively. Exercise capacity, balance and depression were assessed with the 6-min walk test (6MWT), the Timed Up and Go Test (TUG) and the Geriatric Depression Scale (GDS), respectively.

Results: Of the participants, 5% were sarcopenic, 32.8% were frail, and 29.4% had abnormal exercise capacity (6MWT < 82% pred). According to multivariate linear regression analysis, the model consisting of sarcopenia, frailty, TUG and GDS was explained 53% of the variation in 6MWT (R = 0.73, R² = 0.53, p < 0.001), and all variables except GDS were independent predictors of exercise capacity (p < 0.05). Sarcopenia was the strongest predictor of 6MWT (β =-79.76, p = 0.011). The model including sarcopenia, TUG, frailty, and GDS provided 29% prediction of abnormal exercise capacity (Nagelkerke R² = 29.7, p < 0.001), while TUG was the sole significant predictor in the model (Odd Ratio:1.32, p < 0.002), according to logistic regression analysis.

Conclusions: This study indicates that changes in exercise capacity are more influenced by the presence of sarcopenia, and that poor TUG performance is the greatest risk factor for the impaired exercise capacity.