Bibliographic record
Abstract
This is one of the many important conclusions in the report of a large scale, representative survey of the treatment of lesbian, gay, bisexual and transgender (LGBT) people in health and social care services in Britain. The report ‘Unhealthy Attitudes’, which is mentioned in several contributions to this focus issue of the Journal of Research in Nursing (JRN), demonstrates that despite research showing that sexual orientation has an impact on both physical and mental health, services are not adequately meeting the needs of LGBT people (Somerville, 2015). While many western liberal societies have passed significant equality legislation in recent years, it is still the case that heteronormative assumptions pervade social policy, serious gaps in provider knowledge and understanding remain, and harassment and bullying of LGBT patients and staff are not uncommon. While there is then clearly a way to go in achieving equality for LGBT people in health and social care, it is good to see a growing number of nursing researchers taking on board the need systematically to explore the experiences and health outcomes of diverse communities, and make recommendations for equipping healthcare professionals to challenge prejudice and improve care. The papers in this volume present work from four countries across three continents. In the first paper, Laura Legere and Judith MacDonnell use a qualitative approach to explore the healthcare encounters and support needs of lesbian and bisexual cancer survivors in Ontario, Canada. Although based on a small sample of just seven participants, this in-depth exploratory work provides many important insights that could be usefully extended to a larger scale project. The ways in which the persistence of heteronormative assumptions can also disadvantage gay men is central to the second paper in the issue.
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 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 | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".