(028) The Impact of Misinformation on Patient Perceptions Undergoing Sexual Health and Other Urological Procedures
Bibliographic record
Abstract
Abstract Introduction Misinformation and particularly health misinformation, has become rampant in society. Misinformation is defined as the spread of false information irrespective of the intent and has become pervasive due to today’s information channels. The repercussions of misinformation is significant as the World Health Organization declared a COVID-19 ‘infodemic’ in February 2020 as one example in order to combat this phenomenon. The spread of false information among patients has caused for confusion, mistrust with healthcare systems and providers. Although misinformation has been acknowledged on broad scales such as healthcare, there is scarce evidence about the prevalence and effect in certain areas of health and medicine. One particular area of health that may have an inordinate amount of misinformation affecting patient perception is sexual health and urology. Objective The objective of this study was to determine where patients gather information prior to their procedure/consultation and assess their perception as to the reliability of the information. Methods Prior to the consultation/procedure, patients were consented and enrolled to complete a questionnaire consisting of Likert scale, short answer and multiple-choice questions regarding search strategies and perception on misinformation. Demographic, online search strategies and misinformation questions were used to evaluate patient perceptions about general misinformation and the reliability of online information. Cronbach’s alpha was calculated for the Likert questions to assess for internal validity and evaluated on its original five-point scale by a Chi-Square Goodness of Fit Test using R software (v 4.0.3). A p-value < 0.05 was considered statistically significant. Results To date we have enrolled 102 patients. From the survey results, 64.7% patients indicated that they searched up their condition on the internet prior to their consultation/procedure with 86.6% using Google to find relevant information. Short answer questions revealed patients used disease names or descriptive words of their condition when searching for information. Additionally, 40.3% and 43.1% of patients that searched their conditions found the information very reliable or somewhat reliable respectively and was statistically significant. 79.4% and 40.2% of patients spoke with their partner, if applicable, and their friends respectively about their condition. If they did not however, embarrassment to discuss or feeling alone were major reasons to not do so. Patients responded that misinformation is a significant concern when searching up health information. Interestingly, only 4.9% and 56.9% of patients strongly agreed and agreed they were able to identify misinformation and was statistically significant. The majority of patients also strongly agreed or agreed that learning information prior to their appointment affects their relationship with their physician (13.7% and 37.3% respectively). Conclusions Misinformation alters how sexual medicine and urology patients deal with their condition and relationship with their physician. This study is critical to identify areas to create a tailored approach for urologists and sexual medicine specialists to assist in situations where patients may be misinformed about their health. Disclosure No
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".