The aim of this article is to better understand why experts give different scores to the same wines in identical tasting environments. This research focuses on the personal characteristics of experts (or judges), such as their gender, industry credentials, and occupation within the wine industry, and examines how judges respond to their peers\' characteristics. Using a dataset of 5,395 wines judged in the 2022 International Wine and Spirits Competition, we analyse 18,224 scores from different judges. We estimate a series of grade equations at the judge level to understand why a same wine received different scores from judges. A first model makes use of the panel structure of the dataset, incorporates wine fixed effects, and focuses on the personal characteristics of judges. A second model encompasses characteristics of the judging team, without the inclusion of wine fixed effects. At large, on-trade buyers give lower scores than off-trade buyers, as well as female judges compared to male ones. While credentials are not a very significant factor per se, they do have the potential to generate peer effects. Judges tend to be more generous in their assessments when they are assigned to a team with Master of Wine judges. Conversely, they are also consistently more severe when the number of female judges on the team increases. Estimation results converge across sub-datasets, with the exception of sparkling wines. Given the feminisation of the wine industry, in terms of producers, consumers, and experts, the severity of female judges could be beneficial in terms of social welfare.

Explainability is key to enhancing the trustworthiness of artificial intelligence in medicine. However, there exists a significant gap between physicians\' expectations for model explainability and the actual behavior of these models. This gap arises from the absence of a consensus on a physician-centered evaluation framework, which is needed to quantitatively assess the practical benefits that effective explainability should offer practitioners. Here, we hypothesize that superior attention maps, as a mechanism of model explanation, should align with the information that physicians focus on, potentially reducing prediction uncertainty and increasing model reliability. We employed a multimodal transformer to predict lymph node metastasis of rectal cancer using clinical data and magnetic resonance imaging. We explored how well attention maps, visualized through a state-of-the-art technique, can achieve agreement with physician understanding. Subsequently, we compared two distinct approaches for estimating uncertainty: a standalone estimation using only the variance of prediction probability, and a human-in-the-loop estimation that considers both the variance of prediction probability and the quantified agreement. Our findings revealed no significant advantage of the human-in-the-loop approach over the standalone one. In conclusion, this case study did not confirm the anticipated benefit of the explanation in enhancing model reliability. Superficial explanations could do more harm than good by misleading physicians into relying on uncertain predictions, suggesting that the current state of attention mechanisms should not be overestimated in the context of model explainability.

Principles for priority setting in health care are typically forged by experts influenced by the normative literature on priority setting. Meanwhile, their implementation is subject to democratic deliberation, political pressures, and administrative bureaucracy. Sometimes expert proposals are democratically rejected. This points towards a problem: on the one hand, the fact that a majority shares a moral belief does not inherently validate this belief. On the other hand, when justifying a position to others, we cannot expect much success without engaging with their moral judgments. In this work we examine the possibility of including so-called popular views in a reflective equilibrium process. In reflective equilibrium processes, we are usually interested in considered judgments rather than mere intuitions. Popular views, arguably, often do not meet this standard. To mitigate this, we propose to bolster popular views by linking them with theoretical frameworks echoing similar moral perspectives. We use illness severity as a case study and show that a set of popular accounts can provide considered judgments that merit inclusion in a publicly informed reflective equilibrium process. This is plausibly a way forward in the search for priority setting principles that are both normatively sound and acceptable to the public. Our method provides a general framework for refining available data on popular views on moral questions, including when we cannot assess the consideredness of such views.

BACKGROUND: To assess the causal relationship between a medicinal product and a reported event, relevant information needs to be present. Information elements for assessing cases of exposure to medicinal products during pregnancy were predefined and used in a new tool to assess the quality of information. However, the extent in which the presence or absence of these predefined information elements is associated with the overall clinical quality of these cases, as evaluated by pharmacovigilance experts, remains uncertain.
OBJECTIVE: We aimed to validate a novel method to assess the clinical quality of information in real-world pregnancy pharmacovigilance case reports.
METHODS: The clinical quality of case reports regarding medicinal product exposure and pregnancy-related outcomes was appraised from spontaneous reports, literature, Teratology Information Services (UK and Switzerland), The Dutch Pregnancy Drug Register, the Gilenya pregnancy registry and the Enhanced PV programme of Novartis. Assessment was done by means of the novel standardised tool based on the presence and relevance of information, and by expert judgement. The novel tool was validated compared to the expert assessment as the gold standard expressed as the area under the receiver operating characteristic curves, after which the sensitivity and specificity were calculated using cross-tabulations. Inter-rater variability was determined by means of weighted Cohen\'s kappa.
RESULTS: One hundred and eighty-six case reports were included. The clinical quality score as assessed by the novel method was divided into three categories with cut-off values of 45% (poor to intermediate) and 65% (intermediate to excellent). Sensitivity was 0.93 and 0.96 for poor to intermediate and intermediate to excellent, respectively. Specificity was respectively 0.52 and 0.73. Inter-rater variability was 0.65 (95% confidence interval 0.53-0.78) for the newly developed approach, and 0.40 (95% confidence interval 0.28-0.52) for the gold standard assessment.
CONCLUSIONS: The tool described in this study using the presence and relevance of elements of information is the first designed, validated and standardised method for the assessment of the quality of information of case reports in pregnancy pharmacovigilance data. This method confers less inter-rater variability compared with a quality assessment by experts of pregnancy-related pharmacovigilance data.

OBJECTIVE: This study aims to better understand and articulate the pre-assessment judgement processes commonly used by experienced clinical facilitators when assessing nursing students undertaking clinical placement.
BACKGROUND: In the Australian context, clinical facilitators are registered nurses who primarily educate, monitor, support and assess groups of nursing students on clinical placements without carrying a patient load. The duties and scope of clinical facilitators may differ across international and institutional contexts. However, the core concepts of this paper will be relevant despite these differences as the importance of facilitators\' confidence in making pre-assessment judgements of individual nursing student performance while on placement is universally acknowledged. Nursing students are often assessed on their provision of safe practice, patient task-orientated outcomes and professional behaviour. Clearly articulating performance judgements prior to formal assessment is vital to ensure progressive learning of students. Literature reports that many clinical facilitators lack confidence in the art of making performance judgements and call for targeted professional training and support in the clinical assessment of nursing students. To better understand and address this problem, clinical facilitators need a shared understanding of how individual nursing students\' pre-assessment performance judgements are reached during placement experiences.
METHODS: A qualitative case study was used, with data collected via semi-structured interviews. Fifteen Australian clinical facilitators participated, each with over six months of experience.
METHODS: Interview transcripts were analysed through an interpretive-constructivist paradigm. Thematic analysis revealed themes that were then deductively described through the application of the Cognitive Continuum Theory.
RESULTS: Six modes of pre-assessment judgement emerged from the data synthesis process: 1) Recognising patterns, 2) Acknowledging uncertainty, 3) Understanding key players, 4) Verifying or refuting the information, 5) Benchmarking performance and 6) Contextualising information. Each mode is validated through the deductive application of the Cognitive Continuum theory.
CONCLUSIONS: Understanding how experienced clinical facilitators make pre-assessment performance judgements has the potential to increase confidence in performance judgement decisions. In turn, confidence in judgements will increase clinical facilitator\'s capacity to give nursing students feedback that can be explained and justified. The pre-assessment judgement framework also provides a preliminary model for teaching the art of reaching accurate performance judgements to clinical educators in disciplines beyond nursing.

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BACKGROUND: Research indicates new nursing graduates struggle with clinical reasoning, despite some related skill development throughout their education. The question remains, \"Could an earlier focus on clinical reasoning in nursing programs enhance nursing students\' clinical reasoning?\"
OBJECTIVE: This pilot study investigated how first-semester nursing students perceive the use of case studies in clinical reasoning skill development and the perceived benefits of case studies related to the NCSBN Clinical Judgment Measurement Model (NCJMM).
METHODS: Case studies and written reflections were introduced to a convenience sample (n = 45) of first-semester nursing students. Qualitative analysis was conducted on written reflections.
RESULTS: Students found case studies enhanced their knowledge and prepared them for clinical practice as a nurse.
CONCLUSIONS: Findings suggest early introduction of case studies is a meaningful teaching strategy to develop clinical reasoning aligned with the NCJMM.

To prepare students for Next Generation National Council Licensure Examination-Registered Nurse (NCLEX-RN), nurse educators need to develop teaching strategies to foster students\' clinical judgment.
This study examined the effects of unfolding case-based learning (CBL) on baccalaureate nursing students\' clinical judgment upon graduation.
A prospective cohort design was adopted. Students\' self-reported responses to the Lasater Clinical Judgment Rubric were compared between the unfolding CBL (n = 140) and non-CBL (n = 126) cohorts at a school of nursing in the United States.
While students\' responses were similar between the study cohorts, a trend was noted for improved clinical judgment among students in the unfolding CBL cohort. Furthermore, unfolding CBL significantly enhanced students\' perceived proficiency in conducting focused observation.
The findings support early introduction and consistent use of the unfolding CBL throughout the undergraduate nursing program so that students have ample opportunities and contexts to cultivate clinical judgment skills in the classroom.

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BACKGROUND: Whether implicit or explicit, professional judgement is a central component of the many nurse staffing systems implemented in high-income countries to inform workforce planning and staff deployment. Whilst a substantial body of research has evaluated the technical and operational elements of nurse staffing systems, no studies have systematically examined the role of professional judgement and its contribution to decision-making.
OBJECTIVE: To explore nurses\' use of professional judgement in nurse staffing systems in England and Wales.
METHODS: A cross-case comparative design centred on adult in-patient services in three University Health Boards in Wales and three National Health Service Trusts in England. Data generation was undertaken between January 2021 and March 2023 through stakeholder interviews, observations of staffing meetings, and analysis of documents and artefacts. Observations were undertaken in clinical areas but limited to three cases by COVID-19 restrictions. Analysis was informed by translational mobilisation theory.
RESULTS: Two kinds of professional judgement were deployed in the nurse staffing systems: the judgement of clinical nurses and the judgement of senior nurse managers. The research highlighted the reflexive relationship between professional judgement and data, and the circumstances in which organisations placed trust in people and when they placed trust in numbers. Nurses\' professional judgement was central to the generation of data, its interpretation and contextualisation. Healthcare organisations relied on the professional judgements of clinical nurses and senior nurse managers in making operational decisions to mitigate risk, where real-world understanding of the status of the organisation was privileged over formal data. Professional judgement had attenuated authority for the purposes of workforce planning, where data was a master actor. Nurses expressed concerns that strategic decision-making prioritised safety and efficiency, and formal measurement systems did not capture important aspects of care quality or staff wellbeing, which made it difficult to articulate their professional judgement.
CONCLUSIONS: The implementation of staffing systems is resource intensive. Given limited evidence on which to recommend any specific methodology, the priority for future research is to optimise existing systems. If nurses are to deploy their professional judgement to proactively influence the conditions for care, as well as responding to the challenges of risk mitigation, there is a need for robust systems of nursing measurement aligned with agreed standards of care and a vocabulary through which these judgements can be articulated.
CONCLUSIONS: Health systems depend on nurses\' professional judgement for operational staffing decisions, but data is privileged over professional judgement for workforce planning.