A Comprehensive Examination of the Pharmacist’s Role in Mental Illness

The number of people receiving mental illness diagnoses has increased in the last two decades. As one of the most accessible healthcare professionals, pharmacists need to be prepared to assist in the management of patients with mental illness. In this study, the objective was to see how pharmacists’ mental illness knowledge, acceptable social distance, familiarity, and perceived barriers may predict pharmacists’ attitude toward patients with mental illness and also their willingness to provide care to patients with mental illness. The secondary objectives of this study were to test for differences in knowledge, attitude, perceived barriers, and willingness to provide care based on type of degree type of pharmacy, gender, and location. This study utilized a descriptive, cross-sectional survey design to collect study data from a sample of 196 pharmacists. The respondents seemed to have an overall positive attitude towards patients with mental illness. Nearly 60% of pharmacists reported that their pharmacy education adequately trained them to work with patients with mental illness. In regard to education, pharmacists with a PharmD more strongly attributed physical symptoms to mental illness than did BSPharm-trained pharmacists. Most of the pharmacists perceived time available for pharmacist to give attention to patients as one of the most significant barriers. In terms of social distance, most pharmacists expressed that they would be willing to work alongside a person with mental illness than have same person as a babysitter for their child. Although, pharmacists held a somewhat positive perception of patients’ mental illness, there was still a stigma assessed. Due to this, there might be a need for educational interventions that will improve future pharmacists’ willingness to provide care to patients with mental illness

A Comprehensive Examination of the Pharmacist\u27s Role in Mental Illness

The number of people receiving mental illness diagnoses has increased in the last two decades. As one of the most accessible healthcare professionals, pharmacists need to be prepared to assist in the management of patients with mental illness. In this study, the objective was to see how pharmacists\u27 mental illness knowledge, acceptable social distance, familiarity, and perceived barriers may predict pharmacists\u27 attitude toward patients with mental illness and also their willingness to provide care to patients with mental illness. The secondary objectives of this study were to test for differences in knowledge, attitude, perceived barriers, and willingness to provide care based on type of degree type of pharmacy, gender, and location. This study utilized a descriptive, cross-sectional survey design to collect study data from a sample of 196 pharmacists. The respondents seemed to have an overall positive attitude towards patients with mental illness. Nearly 60% of pharmacists reported that their pharmacy education adequately trained them to work with patients with mental illness. In regard to education, pharmacists with a PharmD more strongly attributed physical symptoms to mental illness than did BSPharm-trained pharmacists. Most of the pharmacists perceived time available for pharmacist to give attention to patients as one of the most significant barriers. In terms of social distance, most pharmacists expressed that they would be willing to work alongside a person with mental illness than have same person as a babysitter for their child. Although, pharmacists held a somewhat positive perception of patients\u27 mental illness, there was still a stigma assessed. Due to this, there might be a need for educational interventions that will improve future pharmacists\u27 willingness to provide care to patients with mental illness

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation