Pemodelan Kausal Faktor-Faktor Beban Keluarga dalam Merawat Pasien Kanker Menggunakan Algoritma S2C-Latent

<p class="Abstrak">Pasien kanker memiliki kebutuhan yang kompleks mulai dari masalah fisik, psikologis, sosial dan spiritual. Keluarga yang merawat pasien kanker disebut<em> family car</em><em>egiver</em>. Seorang <em>family caregiver</em> membantu mengatasi hampir semua permasalahan yang dialami pasien baik saat dirawat di rumah maupun di rumah sakit. Keluarga mengalami suka dan duka dalam merawat pasien. Dalam merawat pasien dengan penyakit kronis, bukan hanya pasien tetapi kesejahteraan dan kualitas hidup <em>family car</em><em>egiver</em> juga penting. Oleh karena itu sangat penting untuk mengetahui bagaimana beban family caregiver dan faktor-faktor yang mempengaruhi beban keluarga dalam merawat pasien. Beban <em>family car</em><em>egiver</em> dapat diukur menggunakan <em>Caregiver Reaction Assesment</em> (CRA), yang direpresentasikan oleh beberapa faktor. Dengan memahami hubungan kausal antara faktor-faktor beban keluarga, diharapkan dapat membantu untuk mengidentifikasi bagaimana beban <em>caregiver</em> bersumber dan berdampak. Untuk itu, penelitian ini bertujuan untuk mengidentifikasi hubungan kausal antara faktor-faktor yang berhubungan dengan beban family caregiver dalam merawat pasien. Penelitian ini menggunakan algoritma pemodelan kausal bernama <em>Stable Specification Search for </em><em>Cross-sectional Data with </em><em>Latent </em><em>Variable</em> (S3C-<em>Latent</em>) untuk mendapatkan model kausal antara faktor-faktor beban <em>family car</em><em>egiver</em> yang relevan. Dari hasil analisis pemodelan didapatkan ada 3 faktor yang memiliki hubungan kausal dan 2 faktor memiliki hubungan asosiasi. Gender memiliki hubungan kausal yang stabil terhadap kesiapan kesehatan dan kesiapan dalam merawat<em>.</em> Sedangkan faktor kesiapan merawat mempengaruhi faktor aktivitas <em>family caregiver</em>, selain itu faktor keuangan memiliki hubungan asosiasi yang kuat dengan faktor aktivitas dan hubungan keluarga. Pemodelan kausal ini dapat digunakan sebagai acuan bagi tenaga kesehatan dalam pelayanan kesehatan yang lebih tepat, efisien, dan efektif di dalam menangani permasalahan beban <em>caregiver</em><em>.</em></p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><span lang="IN">Cancer patients have complex needs ranging from physical, psychological, social</span>,<span lang="IN"> and spiritual problems. Families who</span> take<span lang="IN"> care for cancer patients are called family caregivers. A family caregiver helps </span>to <span lang="IN">overcome almost all problems experienced by </span>the <span lang="IN">patients both while being treated at home and in the hospital. Families experience joy and sorrow in caring for patients. In treating patients with chronic diseases, not only the patient but the family caregiver's well-being and quality of life are also important. Therefore, it is very important to know how the family caregiver's burden is and the factors that affect the family burden in caring for patients. Caregiver family burden can be measured using Caregiver Reaction Assessment (CRA), which is represented by several factors. By understanding the causal relationship between family burden factors, it is hoped that it can help to identify how the caregiver burden is sourced and impacted. Therefore, this study aims to identify the causal relationship</span>s<span lang="IN"> between factors related to the burden on family caregivers in caring for patients. This study uses a causal modeling algorithm called Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) to obtain a causal model between the relevant caregiver family load factors. The results of modeling analysis showed that there are 3 factors </span>which<span lang="IN"> have a causal relationship and 2 factors have an association relationship. Gender has a stable causal relationship to health readiness and readiness to care</span>, Moreover, t<span lang="IN">he caring readiness factor affects the family caregiver activity factor, </span>and the <span lang="IN">financial factor </span>has <span lang="IN">a strong association with the activity factor and family relationships. This causal modeling can be used as a reference for health workers so as to give health services which are precise, efficient, and effective in dealing with caregiver burden problems.</span></em></p>

Causal Relationships of Sexual Dysfunction Factors in Women Using S3C-Latent

Women with cancer are at risk for sexual dysfunction characterized by problems with sexual desire, sexual arousal, lubrication, orgasm, sexual satisfaction, and pain during sexual intercourse. The literature review shows that most studies have focused on correlation analysis between factors, and no studies have attempted to identify a causal relationship between factors of sexual dysfunction. This study aims to determine the causal mechanism between factors of sexual dysfunction in cancer patients using a causal algorithm called the Stablespec Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). The causal algorithm has been implemented into the R software package called Stablespec. The computation of the model is done in parallel using the CPU server. The result of this study is that there are a causal relationship and association with a high-reliability score of sexual dysfunction factors. We hope that the causal model obtained can be a scientific reference for doctors and health workers in making decisions so that the quality of life of female cancer patients who experience sexual dysfunction can be improved.

Observational Open Science: An Application to the Literature on Irrelevant Events and Voting Behavior

Replication and transparency are increasingly important in bolstering the credibility of political science research, yet open science tools are typically designed for experiments. For observational studies, current replication practice suffers from an important pathology: just as researchers can often "p-hack" their way to initial findings, it is often possible to "null hack" findings away through specification and case search. We propose an observational open science framework that consists of extending the original time series, independent data collection, pre-registration, multiple simultaneous replications, and collaborators with mixed incentives. We apply the approach to three studies on "irrelevant" events and voting behavior. Each study replicates well in some areas and poorly in others. Had we sought to debunk any of the three with ex post specification search, we could have done so. However, our approach required us to see the full, complicated picture. We conclude with suggestions for future refinements to our approach.

Organizational commitment, psychometric qualities and invariance of the Meyer and Allen Questionnaire for Portuguese Nurses

ABSTRACT Objective: to evaluate the psychometric qualities of the Portuguese version of the Organizational Commitment Questionnaire for the nursing context, through confirmatory analysis and invariance, aiming to evaluate the reliability, internal consistency, construct validity and external validity of the instrument. Method: confirmatory factor analysis of the Portuguese version of the questionnaire was carried out with a sample of 850 nurses, in hospital context. The analysis was complemented using specification search. Goodness of fit was evaluated through different indices. Reliability, internal consistency and construct validity were estimated. The invariance of the model was evaluated in two subsamples of the same sample, in order to confirm the external validity of the factorial solution. Results: the refined model demonstrated good overall fit (χ2/df=6.37; CFI=0.91; GFI=0.92; RMSEA=0.08; MECVI=0.62). The factorial structure was stable (λ:Δχ2(14)=18.31; p=0;193; Intercepts: Δχ2(14)=22.29; p=0.073; Covariance: Δχ2(3)=6.01; p=0.111; Residuals: Δχ2(15)=22.44; p=0.097). Conclusion: the simplified model of the questionnaire demonstrated adequate goodness of fit, representing a stable factorial solution. The instrument was fit to monitor and evaluate the organizational commitment of Portuguese nurses.

Identification and modeling of employee barriers while implementing lean manufacturing in small- and medium-scale enterprises

Purpose The purpose of this paper is to present the factors associated with the employee barriers while implementing lean manufacturing within the small- and medium-scale enterprises (SMEs). Design/methodology/approach The structural equation modelling approach is employed to develop the initial model drawing a sample survey of 133 small and medium enterprises. The result of the study shows that the lack of well-trained and experienced staff, lack of knowledge about existing specialist, cultural resistance to change are acting as the employee barriers while implementing lean manufacturing in SMEs. Then, a further study has been conducted to develop the employee barrier model with these three factors and its contributing variables using specification search representing the sample of 117 small and medium enterprises using the following fit function criteria: Chi-square (C), Chi-square-df (C-df), Akaike information criteria, Browne-Cudeck criterion, Bayes information criterion, Chi-square divided by the degrees of freedom (C/df) and significance level (p). Findings The lack of well-trained and experienced staff, lack of knowledge about existing specialist, and cultural resistance to change with 19 associated elements were considered in the questionnaire. Specification search was carried out to build up the model on the collected data from 117 SMEs. The results of the specification search identified that these three factors with 15 key variables are significant to employee barrier while implementing lean manufacturing in SMEs. Research limitations/implications The limitation of the study was that the sample size of the study was relatively small for further research, large sample size more than 117 are to be expected. Practical implications The present study has explored an unfocused area of lean implementation in small and medium enterprises. The results obtained from the study are expected to help researchers, academics, and professionals for the further studies in the domain of lean manufacturing. Social implications To implement and understand the lean manufacturing system, government of the many countries around the world are helping and encouraging by providing financial assistance for training professionals and establishing professional associations. However, many industries are not successful in lean implementation. This research work provides to develop a strategy to tackle employee barriers for successful lean implementation. Originality/value Very little research has been carried out exploring employee barriers while implementing lean manufacturing in SMEs. This paper will provide value to academics, researchers and practitioners of lean by way of providing insight into significant employee barriers for lean implementation, especially in Indian industries.