Developing a Sexual Orientation and Gender Identity Nursing Education Toolkit
Bibliographic record
Abstract
BACKGROUND: Current education lacks lesbian, gay, bisexual, transgender, questioning, intersex, and two-spirit (LGBTQI2S) content for health care providers (HCPs). Providing HCPs with understanding of LGBTQI2S health issues would reduce barriers. The Innovative Thinking to Support LGBTQI2S Health and Wellness trainee award supported the development of a website with virtual simulation games (VSGs) about providing culturally humble care to LGBTQI2S individuals to address this need. METHOD: An online educational toolbox was developed that included VSGs and resources. Development processes included a visioning meeting, development of learning objectives, and using a decision-point map for script writing. Bilingual VSGs were filmed, and the website was developed. RESULTS: The Sexual Orientation and Gender Identity Nursing Toolkit was created to advance cultural humility in practice. Learning modules focus on encounters using cultural humility to meet the unique needs of the LGBTQI2S community. CONCLUSION: Our innovative educational toolkit can be used to provide professional development of nurses and other HCPs to care for LGBTQI2S individuals. [J Contin Educ Nurs. 2020;51(9):412-419.].
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".