Improving pathways to primary health care among LGBTQ populations and health care providers: key findings from Nova Scotia, Canada
Bibliographic record
Abstract
BACKGROUND: This study explores the perceived barriers to primary health care as identified among a sample of Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) identified individuals and health care providers in Nova Scotia, Canada. These findings, based on a province-wide anonymous online survey, suggest that additional efforts are needed to improve pathways to primary health among LGBTQ populations and in deepening our understanding of how to advance the unique primary health needs of these populations. METHODS: Data were collected from the LGBTQ community through an online, closed-ended anonymous survey. Inclusion criteria for participation were self-identifying as LGBTQ, offering primary health care to LGBTQ patients, being able to understand English, being 16 years of age or older, and having lived in Nova Scotia for at least one year. A total of 283 LGBTQ respondents completed the online survey which included sociodemographic questions, perceptions of respondents' health status, and their primary health care experiences. In addition, a total of 109 health care providers completed the survey based on their experiences providing care in Nova Scotia, and in particular, their experiences and perceptions regarding LGBTQ access to primary health care and physician-patient interactions. RESULTS: Our results indicate that, in several key areas, the primary health care needs of LGBTQ populations in Nova Scotia are not being met and this may in turn contribute to their poor health outcomes across the life course. CONCLUSION: A framework of intersectionality and health equity was used to interpret and analyze the survey data. The key findings indicate the need to continue improving pathways to primary health care among LGBTQ populations, specifically in relation to additional training and related supports for health care providers who work with these populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".