Penerapan Kampanye Penggalangan Dana oleh Perusahaan Penyiaran Televisi untuk Mendukung Kegiatan Kemanusiaan (Studi Deskriptif Kulitatif Program Jembatan Asa Sctv)

Turut berkontribusi terhadap masa depan masyarakat agar menjadi lebih baik merupakan salah satu kewajiban Perusahaan, tak terkecuali Perusahaan yang bergerak dalam bidang industri penyiaran. Penelitian ini membahas kampanye penggalangan dana oleh PT Surya Citra Televisi (SCTV) melalui program Jembatan Asa, sebuah program kemanusiaan untuk membangun jembatan rusak di berbagai daerah di Indonesia. Tujuan penelitian ini adalah untuk mengetahui penerapan kampanye penggalangan dana kemanusiaan Program Jembatan Asa oleh SCTV dengan menggunakan model kampanye Nowak dan Warneryd. Pada model ini terdapat delapan elemen kampanye yang harus diperhatikan: efek yang diharapkan; persaingan komunikasi; obyek komunikasi; populasi target dan kelompok penerima; saluran; pesan; komunikator/pengirim pesan; efek yang dicapai. Pendekatan penelitian yang digunakan adalah kualitatif, dengan sifat penelitian deskriptif. Prosedur pengumpulan data melalui dokumentasi iklan dan berita serta wawancara mendalam dari key informan, yakni Ketua Penyelenggara program CSR Jembatan Asa, Manajer Produksi Berita, Produser Program Liputan 6 dan Video Journalist di Divisi Pemberitaan SCTV. Hasil dan analisis temuan data memperlihatkan bahwa kampanye dilakukan dengan menggandeng stakeholder internal SCTV yaitu departemen pemberitaan, programming, promosi, IT dan media online www.liputan6.com. Sedangkan dari pihak eksternal dilakukan kerja sama dengan Yayasan Relawan Kampung dan Kementerian Sosial Republik Indonesia. Target khalayak yang dituju mengalami Perubahan dari awalnya masyarakat ekonomi kelas A,B,C, diperluas menjadi kelas D dan E, serta kaum muda yang memanfaatkan media online dan media sosial dalam keseharian. Media yang digunakan dalam kampanye terdiri dari traditional media dan new media. Traditional media yang digunakan yaitu program berita Liputan 6, program infotainmen Was-was dan Halo Selebriti, serta program hiburan Inbox yang semuanya tayang di SCTV. Kampenye melalui new media, dilakukan dengan cara publikasi di media online, www.liputan6.com dan www.sctv.co.id. Sedangkan publikasi media sosial dilakukan melalui facebook https://www.facebook.com/Surya.Citra.TV dan twitter @SCTV_. Dengan menggunakan kampanye tersebut, SCTV mampu memperoleh sumbangan dana masyarakat sebesar Rp 4.546.241.368, yang digunakan untuk membangun 10 jembatan, melebihi target awal kampanye yang direncanakan hanya membangun 3 jembata

Process evaluation of appreciative inquiry to translate pain management evidence into pediatric nursing practice

Background Appreciative inquiry (AI) is an innovative knowledge translation (KT) intervention that is compatible with the Promoting Action on Research in Health Services (PARiHS) framework. This study explored the innovative use of AI as a theoretically based KT intervention applied to a clinical issue in an inpatient pediatric care setting. The implementation of AI was explored in terms of its acceptability, fidelity, and feasibility as a KT intervention in pain management. Methods A mixed-methods case study design was used. The case was a surgical unit in a pediatric academic-affiliated hospital. The sample consisted of nurses in leadership positions and staff nurses interested in the study. Data on the AI intervention implementation were collected by digitally recording the AI sessions, maintaining logs, and conducting individual semistructured interviews. Data were analysed using qualitative and quantitative content analyses and descriptive statistics. Findings were triangulated in the discussion. Results Three nurse leaders and nine staff members participated in the study. Participants were generally satisfied with the intervention, which consisted of four 3-hour, interactive AI sessions delivered over two weeks to promote change based on positive examples of pain management in the unit and staff implementation of an action plan. The AI sessions were delivered with high fidelity and 11 of 12 participants attended all four sessions, where they developed an action plan to enhance evidence-based pain assessment documentation. Participants labeled AI a 'refreshing approach to change' because it was positive, democratic, and built on existing practices. Several barriers affected their implementation of the action plan, including a context of change overload, logistics, busyness, and a lack of organised follow-up. Conclusions Results of this case study supported the acceptability, fidelity, and feasibility of AI as a KT intervention in pain management. The AI intervention requires minor refinements (e.g., incorporating continued follow-up meetings) to enhance its clinical utility and sustainability. The implementation process and effectiveness of the modified AI intervention require evaluation in a larger multisite study

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.

A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome

We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS).status: publishe

Influences of nurses' scoring of children's postoperative pain

There is a lack of clarity as to why some nurses are not delivering optimal pain management to children post-operatively. This retrospective chart review study examined nurses’ pain scoring on 175 children during the first 24 hours post-operatively. Data were analysed on the amount of assessments made, assessment scores recorded, as well as the age, gender and type of surgery performed. One-quarter of children had no assessment record of their pain in the first 24 hours post-operatively. When the pain tool was part of an observation chart, nurses recorded more pain scores. Nurses’ scoring of children’s pain is influenced positively by children under five years of age and those who undergo abdominal surgery. Nurses who had access to one document for recording vital signs as well as pain scores were more likely to assess and record a child’s pain score than nurses who had to use a separate chart

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

PURPOSE: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. METHODS AND MATERIALS: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. RESULTS: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. CONCLUSIONS: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan