Conducting a Continuing Education Course for Healthcare Professionals on LGBT+ Healthcare Assistance Using a M-Health Solution: A Pilot Study
Bibliographic record
Abstract
Addressing healthcare disparities within the LGBT+ community necessitates healthcare professionals (HCP) be better prepared to meet healthcare needs of diverse LGBT+ patients. This article aimed to verify the potential of an m-Health application "Over the Rainbow" to improve the knowledge of healthcare providers and evaluate user experiences of the app. We conducted a quantitative, pilot, and usability study involving healthcare students and professionals from both private and public health systems in Brazil. Data were collected via snowball sampling from June to December 2023. The study utilized a sociodemographic questionnaire, the Training Needs Analysis (TNA) tool, a pre- and posttest knowledge questionnaire, and the User Experience Questionnaire (UEQ). A total of 29 participants answered all questions, with the majority identifying as female (89.7%), cisgender (100%), heterosexual (86.2%), most participants were nurses (students, 37.9%, nursing technicians, 24.1%) and from the South region of Brazil (65.5%). Only 13.8% of participants acknowledged having received prior continuing education related to LGBT+ health. The TNA indicated neutral to moderate knowledge background in the LGBT+ area. Regarding the UEQ, participants reported positive experiences with the m-Health application. The continuing education course utilizing an m-Health solution presented an opportunity to enhance competency in caring for LGBT+ individuals within healthcare settings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".