Dataset Information

Peer support: A needs assessment for social support from trained peers in response to stress among medical physicists.

ABSTRACT: PURPOSE:Previous studies suggest that within radiation oncology, medical physicists (MP) experience high workloads. Little is known about how MPs use social support (SS) in times of stress. METHODS:In collaboration with the Workgroup on Prevention of Medical Error, the American Association of Physicists in Medicine administered this Human Investigation Committee (HIC) approved email survey to 8566 members. Respondents were considered likely to seek SS if they answered (probably/definitely would) and unlikely to seek support if they answered (probably/definitely would not). Logistic regression was applied to determine associations between demographic factors and willingness to seek support as well as perception of barriers. RESULTS:One thousand two hundred and ninety-seven members (15.1%) accessed and gave consent for the survey. One thousand and one (11.7%) respondents answered all relevant questions. Respondents were predominantly male (69.1%), MP in radiation oncology (81.8%), private practice (51.6%), with practice duration> 10 yr (60.2%). MPs were likely to seek SS for personal physical illness (78.63%), involvement in a medical error (73.94%) or adverse patient outcome (75.17%). MPs sought SS in the setting of personal fatigue (33.2%) or burnout (44.3%). Barriers to seeking SS were lack of time (80.3%), and uncertainty about whom to access (70.7%). MPs responded that they would be most likely to seek support from an equally experienced medical physicist colleague (81.0%). Most MPs (67.0%) identified as having experienced stressors, with serious family illness (35.2%), or burnout (32.8%) being most common. Factors associated with MPs unwillingness to seek SS for medical error included> 20 yr in practice (vs still in training - OR 0.30, P = 0.015), and male gender (OR 0.60, P = 0.003). Male gender was associated with the lowest willingness to seek support (OR 2.10, P = 0.0001), but also with fewer perceived barriers (OR 1.60, P = 0.0075). CONCLUSION:Willingness to seek SS is demonstrated, and MPs want colleagues to provide support. Given these results, peer support could be considered among MPs.

SUBMITTER: Johnson J

PROVIDER: S-EPMC6753865 | biostudies-other | 2019 Sep

REPOSITORIES: biostudies-other

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Peer support: A needs assessment for social support from trained peers in response to stress among medical physicists.

Johnson Jennifer J Ford Eric E Yu James J Buckey Courtney C Fogh Shannon S Evans Suzanne B SB

Journal of applied clinical medical physics 20190729 9

<h4>Purpose</h4>Previous studies suggest that within radiation oncology, medical physicists (MP) experience high workloads. Little is known about how MPs use social support (SS) in times of stress.<h4>Methods</h4>In collaboration with the Workgroup on Prevention of Medical Error, the American Association of Physicists in Medicine administered this Human Investigation Committee (HIC) approved email survey to 8566 members. Respondents were considered likely to seek SS if they answered (probably/de ...[more]

PMID: 31355990

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Project description:BackgroundWhile online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge.ObjectiveThe objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs.MethodsWe initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features.ResultsThe best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others.ConclusionsWe developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

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Peer support: A needs assessment for social support from trained peers in response to stress among medical physicists.

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Peer support: A needs assessment for social support from trained peers in response to stress among medical physicists.

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