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Record W2239106233 · doi:10.1200/jop.2015.006429

ReCAP: Social Media Use Among Physicians and Trainees: Results of a National Medical Oncology Physician Survey

2016· article· en· W2239106233 on OpenAlexaffabout
Rachel Adilman, Yanchini Rajmohan, Edward G. Brooks, Gloria Roldan Urgoiti, Caroline Chung, Nazik Hammad, Martina Trinkaus, Madiha Naseem, Christine Simmons

Bibliographic record

VenueJournal of Oncology Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsPrincess Margaret Cancer CentreQueen's UniversityUniversity of WaterlooUniversity of CalgarySt. Michael's Hospital
Fundersnot available
KeywordsSocial mediaMentorshipMedicineOncologyInternal medicineDescriptive statisticsHealth careFamily medicineMedical educationWorld Wide WebStatistics

Abstract

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QUESTION ASKED: To what extent, and for what purpose, do oncology physicians and physicians-in-training use Web-based social media? SUMMARY ANSWER: Despite the ability of social media to enhance collaboration and knowledge dissemination among health care providers, this cohort survey study identified an overall low use of social media among oncologists, and significant generational gaps and differences in patterns of use. METHODS: A nine-item survey was designed using a survey-generating Web site (SurveyMonkey) and was distributed securely via weekly e-mail messages to 680 oncology physicians and physicians-in-training from July 2013 through September 2013. All responses were received anonymously. Results were analyzed and are reported using descriptive statistics. RESULTS: Of 680 surveys sent, 207 were completed, for a response rate of 30.4%. Social media were used by 72% of our survey respondents (95% CI, 66% to 78%; Table 1 ). Results were cross tabulated by age, which revealed a significant difference in social media use by age group, with 89% of trainees, 93% of fellows, and 72% of early-career oncologists reporting social media use, compared with only 39% of mid-career oncologists (P < .05). Respondents reported using each social media platform for either personal or professional purposes, but rarely both. When respondents were questioned regarding barriers to social media use and their hesitations around joining a medically related social media site, the majority (59%) answered, “I don't have enough time.” [Table: see text] BIAS, CONFOUNDING FACTOR(S), DRAWBACKS: This study was conducted online, via e-mail. Therefore, respondents may represent a subpopulation of individuals who already prefer using Web-based technologies and may be more inclined to use social media, compared with individuals who do not use e-mail and were, by default, excluded from the study. We assumed, in designing this study, that the proportion of practicing oncology physicians who do not use e-mail is low. Although our sample size is small, it does represent one third of all registered medical oncologists in Canada. Finally, the high percentage of medical oncologist respondents and the concomitantly low fraction of respondents from other specialties may mean these results are more telling of social media habits in the aforementioned demographic rather than other oncology specialties. REAL-LIFE IMPLICATIONS: Our study revealed that oncology physicians and physicians-in-training who participate in Web-based social networking are largely within the younger age cohorts, whereas mid-career oncologists (age 45 to 54 years) are largely absent from the social media scene. Gaps in social networking use between younger physicians and trainees and older generations of physicians may result in critical gaps in communication, collaboration, and mentorship between these demographics. It is hoped that with further research into understanding patterns of use and limitations, medical professionals and trainees may increase their use of social media for networking, education, mentorship, and improved patient care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.210
GPT teacher head0.490
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations84
Published2016
Admission routes2
Has abstractyes

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