Experiences and Perspectives of Filipino Patients with Stroke on Physical Therapy Telerehabilitation: A Phenomenological Study Protocol

Introduction: Stroke is the third leading cause of death in the Philippines, so these patients must continuously undergo rehabilitation for faster recovery. With the rise of COVID-19, physical therapy (PT) telerehabilitation (TR) has emerged, where services are provided outside the usual rehabilitation setting for patients with stroke to continue their treatment while reducing the risk of acquiring COVID-19. However, it is a relatively new service in the country; hence, further research is needed to identify the factors and needs of these patients during TR, which may help improve PT TR services. Objective: This study aims to explore the experiences and perspectives of Filipino patients with stroke who have undergone PT TR in the Philippines since March 2020. Administrators of healthcare facilities, policy-makers, and other decision-makers involved in evaluating, implementing, and developing PT TR may benefit patients with stroke. This can expand the scope of rehabilitation to patients with stroke who have no access to face-to-face rehabilitation or improve the training or education of Physical Therapists who are providing TR to stroke patients. Methods: This will be a qualitative phenomenological study design that will use purposive sampling to recruit participants. Semi-structured interviews (SSI) will be conducted online using Google Meetings®, Zoom®, or Facebook Messenger® to record their experiences and perspectives. The NVivo data analysis software will be used to create codes and identify themes from the data gathered. The data that will be obtained is about the experiences and perspectives of Filipino patients with stroke regarding PT TR. The insights of the participants will undergo Thematic Analysis until no new information will be discovered from the analyzed data

A qualitative study on the work-life balance of solo mother night-shift employees in the business process outsourcing industry

The Business, Process Outsourcing (BPO) industry is thriving in the Philippines, yet many Filipinos struggle to work in BPO companies especially those who are working the night shift. This research study used phenomenological analysis on the work-life balance of solo mothers who work the night shift in the BPO industry. The researchers aimed to understand the lived experiences of these solo mothers. The seven participants ranged 18-35 years old and have a child who is 10 years old or below. Data was collected using semi-structured interviews. Results have shown that the common themes among the participants are Conflicting Work and Life Roles, Struggles at Work, Compensation for Work, Sacrifice, Preparation of the Child\u27s Future, Prioritizing the Child, and Financially Independent. Theories such as work-life conflict and conflict theory were included in the discussion and was associated with the results

Machine learning for cell type classification from single nucleus RNA sequencing data

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines

Classification performance between the default and optimal hyperparameter settings.

The 3.5 CV feature set was used and models produced using default and optimal hyperparameter setting. F-beta was calculated as a measure of classification accuracy. Log2 size is log base 2 of the cluster size. The labeled p-values for each method are from the Wilcoxon signed-rank test between the default and optimal validation.</p

Classification performance using two feature selection methods (CV vs BIN filtering) and five machine learning methods.

For feature selection using coefficient of variation (CV), the filtering thresholds from left to right were 0.52, 1.5, 2.5, 3.5, and 4.5. For binary score (BIN), the filtering thresholds from left to right are 0.15, 0.10, 0.05, and 0.01. The resulting number of genes for each threshold is listed below the threshold labels. Each of these feature sets was used by five different machine learning methods (LightGBM, Neural Network, SVM, Logistic Regression, Random Forest) using the training data. F-beta was calculated as a measure of classification accuracy.</p

Model performance on the kidney dataset.

Four models (Binary Logistic Regression, Multinomial Logistic Regression, Neural Networks, and LightGBM) were built using the optimal hyperparameters trained from the MTG dataset. Eight CV thresholds (1.0, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, and 7.5) were applied for feature selection. The red horizontal line indicates the median F-beta value for the best CV threshold for a given method from the MTG dataset.</p

Overview of the machine learning pipeline.

A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly split into training (60%), validation (20%), and test (20%) sets independently for each cell type. The training sets are used to train the models. The validation set provides an initial test for accuracy of the trained models and is used to adjust the model’s hyperparameters. Once the hyperparameters are optimized, the test set is run through each model and the F-beta score distribution across all clusters is used for model comparison.</p

Machine learning hyperparameters.

Machine learning hyperparameters.</p

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Model performance on training, validation, and test datasets.

CV thresholds of 2.5 were used for Multinomial Logistic Regression and Neural Networks while a threshold of 3.5 was used for all other models. F-beta was calculated as a measure of classification accuracy for the training, validation, and test datasets. Log2 size is log base 2 of the cluster size. Differences between these distributions highlight the effect of overfitting.</p