BackgroundAs the field of D&I (dissemination and implementation) science grows to meet the need for more effective and timely applications of research findings in routine practice, the demand for formalized training programs has increased concurrently. The Mentored Training for Dissemination and Implementation Research in Cancer (MT-DIRC) Program aims to build capacity in the cancer control D&I research workforce, especially among early career researchers. This paper outlines the various components of the program and reports results of systematic evaluations to ascertain its effectiveness.MethodsEssential features of the program include selection of early career fellows or more experienced investigators with a focus relevant to cancer control transitioning to a D&I research focus, a 5-day intensive training institute, ongoing peer and senior mentoring, mentored planning and work on a D&I research proposal or project, limited pilot funding, and training and ongoing improvement activities for mentors. The core faculty and staff members of the MT-DIRC program gathered baseline and ongoing evaluation data regarding D&I skill acquisition and mentoring competency through participant surveys and analyzed it by iterative collective reflection.ResultsA majority (79%) of fellows are female, assistant professors (55%); 59% are in allied health disciplines, and 48% focus on cancer prevention research. Forty-three D&I research competencies were assessed; all improved from baseline to 6 and 18 months. These effects were apparent across beginner, intermediate, and advanced initial D&I competency levels and across the competency domains. Mentoring competency was rated very highly by the fellows––higher than rated by the mentors themselves. The importance of different mentoring activities, as rated by the fellows, was generally congruent with their satisfaction with the activities, with the exception of relatively greater satisfaction with the degree of emotional support and relatively lower satisfaction for skill building and opportunity initially.ConclusionsThese first years of MT-DIRC demonstrated the program’s ability to attract, engage, and improve fellows’ competencies and skills and implement a multicomponent mentoring program that was well received. This account of the program can serve as a basis for potential replication and evolution of this model in training future D&I science researchers.Electronic supplementary materialThe online version of this article (10.1186/s13012-018-0711-3) contains supplementary material, which is available to authorized users.
Background: Despite recently expanded access to health insurance, consumers still face barriers to using their coverage to obtain needed health care. Objective: To examine the characteristics of those who delay or avoid health care due to costs. Methods: Participants were recruited via Amazon MTurk and completed a survey assessing demographic characteristics, financial toxicity, health care minimizer-maximizer tendencies, health insurance knowledge, numeracy, delaying/avoiding any care, and delaying/avoiding six common health care services (three preventive and three nonpreventive services). Validated measures were used when available. Delay/avoidance behaviors were categorized into delaying/avoiding any care, preventive care, and nonpreventive care. Logistic regression models examined 1) financial toxicity, 2) minimizer-maximizer tendencies, 3) numeracy, 4) health insurance knowledge, and 5) knowledge of preventive care coverage separately on three forms of delay/avoidance behaviors, controlling for chronic conditions, insurance status, and/or income where appropriate. Results: Of 518 respondents, 470 did not fail attention-check questions and were used in analyses. Forty-five percent of respondents reported delaying/avoiding care due to cost. Multivariable analyses found that financial toxicity was related to delaying/avoiding any care (odds ratio [OR] = 0.884, P < 0.001), preventive care (OR = 0.906, P < 0.001), and nonpreventive care (OR = 0.901, P < 0.001). A tendency to minimize seeking health care (OR = 0.734, P < 0.001) and lower subjective numeracy (OR = 0.794, P = 0.023) were related to delaying/avoiding any care. General health insurance knowledge (OR = 0.989, P = 0.023) and knowledge of preventive care coverage (OR = 0.422, P < 0.001) were related to delaying/avoiding preventive care. Conclusions: Many people delay or avoid health care due to costs, even when insured. Results suggest that there may be different reasons individuals delay or avoid preventive and nonpreventive care. Findings may inform interventions to educate consumers and support discussions about health care costs to facilitate appropriate health care utilization.
BackgroundBreast cancer is the most commonly diagnosed malignancy in women. Mastectomy and breast-conserving surgery (BCS) have equivalent survival for early stage breast cancer. However, each surgery has different benefits and harms that women may value differently. Women of lower socioeconomic status (SES) diagnosed with early stage breast cancer are more likely to experience poorer doctor-patient communication, lower satisfaction with surgery and decision-making, and higher decision regret compared to women of higher SES. They often play a more passive role in decision-making and are less likely to undergo BCS. Our aim is to understand how best to support women of lower SES in making decisions about early stage breast cancer treatments and to reduce disparities in decision quality across socioeconomic strata.MethodsWe will conduct a three-arm, multi-site randomized controlled superiority trial with stratification by SES and clinician-level randomization. At four large cancer centers in the United States, 1100 patients (half higher SES and half lower SES) will be randomized to: (1) Option Grid, (2) Picture Option Grid, or (3) usual care. Interviews, field-notes, and observations will be used to explore strategies that promote the interventions’ sustained use and dissemination. Community-Based Participatory Research will be used throughout. We will include women aged at least 18 years of age with a confirmed diagnosis of early stage breast cancer (I to IIIA) from both higher and lower SES, provided they speak English, Spanish, or Mandarin Chinese. Our primary outcome measure is the 16-item validated Decision Quality Instrument. We will use a regression framework, mediation analyses, and multiple informants analysis. Heterogeneity of treatment effects analyses for SES, age, ethnicity, race, literacy, language, and study site will be performed.DiscussionCurrently, women of lower SES are more likely to make treatment decisions based on incomplete or uninformed preferences, potentially leading to poorer decision quality, quality of life, and decision regret. This study hopes to identify solutions that effectively improve patient-centered care across socioeconomic strata and reduce disparities in decision and care quality.Trial registrationNCT03136367 at ClinicalTrials.govProtocol version: Manuscript based on study protocol version 2.2, 7 November 2017.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5109-2) contains supplementary material, which is available to authorized users.
This qualitative study explored cancer survivors’ experiences selecting and using health insurance and anticipating out-of-pocket care costs. Thirty individuals participated in semistructured interviews. On average, participants were 54 years ( SD ± 8.85, range 34-80) and diagnosed with cancer about 5 years prior (range 0.5-10 years). About 57% were female, 77% were non-Hispanic White, and 53% had less than a college education. Participants struggled to access information about health insurance and costs. Lack of cost transparency made it difficult to anticipate expenses and increased anxiety. Many participants were surprised that after cancer, care that was once preventive with no out-of-pocket costs became diagnostic with associated fees. They discussed the cognitive burden of managing finances on top of treatment and overseeing communication between doctors and insurance. Interventions are needed to clearly communicate information about insurance coverage and care costs to improve cancer survivors’ confidence in selecting health insurance and anticipating out-of-pocket expenses.
Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.
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