Risk factors to mental health challenges among the LGBTI+ community in Gaborone, Botswana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Lesbian, Gay, Bisexual, Transgender, Intersex and other gender diverse groupings symbolised by + (LGBTI+) individuals experience adverse mental health problems, and several factors have been documented to facilitate such problems. However, in Botswana, the factors facilitating LGBTI+ individuals to experience mental health challenges have not been explored with previous studies only highlighting the poor mental health outcomes they experience. OBJECTIVES: The aim of the study was to explore and describe factors that could cause mental health challenges in LGBTI+ individuals in Gaborone, Botswana. METHOD: A qualitative, descriptive, phenomenological design was employed to examine the research question. In data collection, 15 unstructured in-depth telephonic interviews were conducted until data saturation. Data were analysed with a co-coder using the data analysis method by Colaizzi. RESULTS: Three themes emerged following data analysis and were reasons for experiencing mental health challenges, experiences of challenges in accessing healthcare services and the social challenges of everyday life. CONCLUSION: The findings indicate that a variety of factors influence the mental health problems in some LGBTI+ individuals.Contribution: The knowledge of the factors that cause LGBTI+ individuals' mental health challenges can inform mental healthcare to be rendered. The findings can apprise nursing curriculum development and policy regarding the needs of LGBTI+ individuals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it