A continuing digital education in LGBT+ care for healthcare professionals using a m-Health App
Bibliographic record
Abstract
Sexuality and gender continue to be taboos in contemporary societies, especially within populations whose identities do not identify with heteronormativity. Aging occurs at the intersections of those who identify as Lesbian, Gay, Bisexual, Transvestite, Transsexual and Transgender (LGBT+). Lack of specific knowledge about assistance to the LGBT+ public contributes to fragmented and prejudiced. Given the above, the general objective of this study was to develop a continuing education course using an m-Health application to improve the training of health professionals on knowledge specific to health care for LGBT+ individuals. To respond to proposed general objective, three empirical studies were carried out. The first scientific study (chapter 3) aimed to describe the perceptions of Brazilian LGBT+ community about their aging process and their experiences in accessing healthcare. Furthermore, to create greater awareness and new knowledge about this perception, an analysis was carried out arts-based supplementary qualitative, focused on participants in Brazilian LGBT+ community. The sample consisted of 116 adults. The widespread discrimination has been experienced by LGBT+ individuals in several aspects of life. Participants could not live authentically and felt compelled to hide or deny their sexual and gender identity to access healthcare equitably. The second scientific study (chapter 4) aimed to discuss the development of an education course continued for healthcare professionals to provide healthcare assistance competent to the Brazilian LGBT+ population and the implementation of this course using an m-Health solution. The continuing education course, "Ally: A Holistic Approach to the LGBT+ Individual" was developed based on the Bloom's taxonomy, in Nurses¿s Health Education for LGBT Seniors (HEALE), in the manual "Implementing Curricular and Institutional Climate Changes to Improve Health Care for Individuals who are LGBT, Gender Nonconforming, or Born with DSD", in the National LGBT Comprehensive Health Policy, and other literature complementary. This course has six modules and was offered through application called "Beyond the Rainbow". The third scientific study (chapter 5) aimed to determine the knowledge acquired, acceptability and usability of the m-Health application. This was a quantitative study, cross-sectional and pilot involving students and health professionals from private and public health systems in Brazil. A total of 42 participants answered all the questions. Only 16.7% of participants reported have received prior continuing education in the LGBT+ area. The instrument of Training Needs Analysis indicated a low level of need training in this sample. There was no statistical significance between the results of pre- and post-course questionnaires. Regarding the Questionnaire User Experience,feedback was collected from 29 participants, who reported positive experiences with the m-Health app. The education course 14 continued use of an m-Health solution presents an opportunity to fill the gap in specific knowledge about health care for LGBT+ population. By engaging in continuing education, health professionals health can through the expansion and propagation of knowledge, practices and reflections on the work process, better understand the needs and unique experiences of LGBT+ individuals, allowing them to provide a care that is both equitable and competent in its daily practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".