Designing Implementation Strategies for the Inclusion of Lesbian, Gay, Bisexual, Transgender, Intersex, Queer, and Allied and Key Populations’ Content in Undergraduate Nursing Curricula in KwaZulu-Natal, South Africa: Protocol for a Multimethods Research Project
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, intersex, queer, and allied (LGBTQIA+) individuals encounter challenges with access and engagement with health services. Studies have reported that LGBTQIA+ individuals experience stigma, discrimination, and health workers' microaggression when accessing health care. Compelling evidence suggests that the LGBTQIA+ community faces disproportionate rates of HIV infection, mental health disorders, substance abuse, and other noncommunicable diseases. The South African National Strategic Plan for HIV or AIDS, tuberculosis, and sexually transmitted infections, 2023-2028 recognizes the need for providing affirming LGBTQIA+ health care as part of the country's HIV or AIDS response strategy. However, current anecdotal evidence suggests paucity of LGBTQIA+ and key populations' health content in the undergraduate health science curricula in South Africa. Moreover, literature reveals a general lack of health worker training regarding the health needs of LGBTQIA+ persons and other key populations such as sex workers, people who inject drugs, and men who have sex with men. OBJECTIVE: This study aimed to describe the design of a project that aims at facilitating the inclusion of health content related to the LGBTQIA+ community and other key populations in the undergraduate nursing curricula of KwaZulu-Natal, South Africa. METHODS: A multimethods design encompassing collection of primary and secondary data using multiple qualitative designs and quantitative approaches will be used to generate evidence that will inform the co-design, testing, and scale-up of strategies to facilitate the inclusion of LGBTQIA+ and key populations content in the undergraduate nursing curricula in KwaZulu-Natal, South Africa. Data will be collected using a combination of convenience, purposive, and snowball sampling techniques from LGBTQIA+ persons; academic staff; undergraduate nursing students; and other key populations. Primary data will be collected through individual in-depth interviews, focus groups discussions, and surveys guided by semistructured and structured data collection tools. Data collection and analysis will be an iterative process guided by the respective research design to be adopted. The continuous quality improvement process to be adopted during data gathering and analysis will ensure contextual relevance and sustainability of the resultant co-designed strategies that are to be scaled up as part of the overarching objective of this study. RESULTS: The proposed study is designed in response to recent contextual empirical evidence highlighting the multiplicity of health challenges experienced by LGBTQIA+ individuals and key populations in relation to health service delivery and access to health care. The potential findings of the study may be appropriate for contributing to the education of nurses as one of the means to ameliorate these problems. Data collection is anticipated to commence in June 2024. CONCLUSIONS: This research has potential implications for nursing education in South Africa and worldwide as it addresses up-to-date problems in the nursing discipline as it pertains to undergraduate students' preparedness for addressing the unique needs and challenges of the LGBTQIA+ community and other key populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52250.
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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 | 0.077 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 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".