Accuracy and self correction of information received from an internet breast cancer list: content analysis.

OBJECTIVES: To determine the prevalence of false or misleading statements in messages posted by internet cancer support groups and whether these statements were identified as false or misleading and corrected by other participants in subsequent postings. DESIGN: Analysis of content of postings. SETTING: Internet cancer support group Breast Cancer Mailing List. MAIN OUTCOME MEASURES: Number of false or misleading statements posted from 1 January to 23 April 2005 and whether these were identified and corrected by participants in subsequent postings. RESULTS: 10 of 4600 postings (0.22%) were found to be false or misleading. Of these, seven were identified as false or misleading by other participants and corrected within an average of four hours and 33 minutes (maximum, nine hours and nine minutes). CONCLUSIONS: Most posted information on breast cancer was accurate. Most false or misleading statements were rapidly corrected by participants in subsequent postings.

Understanding treatment-related symptoms, symptom management techniques, and symptom management self-efficacy in a sample of non-muscle invasive bladder cancer patients

The current literature available on bladder cancer symptom management from the perspective of the patients themselves is limited. There is also limited psychosocial research specific to bladder cancer patients and no previous studies have developed and validated measures for bladder cancer patients’ symptom management self-efficacy. The purpose of this study was to investigate non-muscle invasive bladder cancer patients’ health related quality of life through two main study objectives: (1) to describe the treatment related symptoms, reported effectiveness of symptom-management techniques, and the advice a sample of non-muscle invasive bladder cancer patients would convey to physicians and future patients; and (2) to evaluate Lepore’s symptom management self-efficacy measure on a sample of non-muscle invasive bladder cancer patients. Methods. A total of twelve (n=12) non-muscle invasive bladder cancer patients participated in an in-depth interview and a sample of 46 (n=4) non-muscle invasive bladder cancer patients participated in the symptom-management self-efficacy survey. Results. A total of five symptom categories emerged for the participants’ 59 reported symptoms. Four symptom management categories emerged out of the 71 reported techniques. A total of 62% of the participants’ treatment related symptom-management techniques were reported as effective in managing their treatment-related symptoms. Five advice categories emerged out of the in-depth interviews: service delivery; medical advice; physician-patient communication; encouragement; and no advice. An exploratory factor analysis indicated a single-factor structure for the total population and a multiple factor structure for three subgroups: all males, married males, and all married participants. Conclusion. These findings can inform physicians and patients of effective symptom-management techniques thus improving patients’ health-related quality of life. The advice these patients’ impart can improve service-delivery and patient education.^

The Impact of a Brief Intervention on Medical Students' Readiness to Screen for DV - Changes in Decisional Balance and Self-Efficacy

Introduction: Domestic violence (DV) affects millions of Americans each year. Yet, there is a dearth of theory-based DV curricula, resulting in physicians unprepared to screen for DV. This study utilized a survey based on constructs from the Transtheoretical Model to assess the readiness of first- and second-year medical students to screen for DV. Information from this survey will be used to help institutionalize DV education into the medical curriculum. [See PDF for complete abstract]

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Integrating diabetes self-management with the health goals of older adults: A qualitative exploration

Objective. This study investigates the life and health goals of older adults with diabetes, and explores the factors that influence their diabetes self-management. Methods: Qualitative in-depth interviews were conducted with 24 older adults with diabetes and other morbid conditions and/or their caregivers, when appropriate. ^ Results. Participants’ provided a consistent set of responses when describing life and health goals. Participants described goals for longevity, better physical functioning, spending time with family, or maintaining independence. Diabetes discordant conditions, but not diabetes, were seen as barriers to life goals for participants with functional impairments. Functionally independent participants described additional health goals that related to diabetes self-management as diabetes was seen often a barrier to life goals. Caregivers, co-morbid conditions, denial and retirement were among the factors that influenced initiation of diabetes self-management. ^ Conclusion. Participants endorsed health goals and diabetes self-management practices that they believed would help them accomplish their life goals. Functional capabilities and social support were key factors in the relationship between diabetes self-management and their broader goals. ^ Practice implications. When planning diabetes treatments, clinicians, patients and caregivers should discuss the relationship between diabetes self-management and health and life goals as well as the affects of functional limitations and caregiver support.^