An assessment of the attitudes, knowledge, and education regarding the health care needs of LGBTQ patients with cancer: results of an ESMO/SIOPE global survey
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals with cancer have specific and unique health issues and needs. Reports persist of inequalities in the care provided for these patients, making it important to assess the attitudes and knowledge of LGBTQ needs among those who provide care. MATERIALS AND METHODS: The European Society for Medical Oncology (ESMO) and the European Society for Paediatric Oncology (SIOP Europe) Adolescents and Young Adults Working Group designed this survey comprising 67 questions covering demographics, knowledge, and education of LGBTQ health needs, and attitudes regarding LGBTQ patients with cancer. RESULTS: Among the 672 respondents, a majority do not ask about sexual orientation and gender identity during first visit (64% and 58%, respectively). Only a minority of the respondents considered themselves well informed regarding gay/lesbian and transgender patients' health (44% and 25%, respectively) and psychosocial needs (34%). There was high interest in receiving education regarding the unique health needs of LGBTQ patients (73%). CONCLUSIONS: Survey respondents indicated a willingness to provide care to LGBTQ patients, but a lack of confidence in the knowledge of the health issues and needs of LGBTQ individuals. Lack of training provided in medical schools and postgraduate training programmes and strong interest for additional education on these issues were reported.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".