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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,077 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».