Health Support Directed at Lesbian, Gay and Bisexuals: Socio-demographic Context and Education
Bibliographic record
Abstract
Introduction: Promoting the dignity of people and equality of access to care are two fundamental pillars of good healthcare practice. Thus, producing evidence on educating and investigating competencies and practices aimed at lesbian, gay and bisexual (LGB) clients and their determinants constitute synergetic strategies which are necessary to ensure excellent health care for this particular group. Objective: To analyse the effects of the socio-demographic conditions and training in the care competence and practices carried out by health professionals for lesbian, gay and bisexual clients. Methodology: Descriptive study carried out on a sample of 119 Portuguese health professionals, the majority of whom are female with an average age of 37.90 years. Instruments:Sexual Orientation Counselor Competency Scale Citation (Bidell,2005) Correlates of Homophobia and Gay Affirmative Practice in Rural Practitioners (Crisp,2002), adapted by Pereira & Cunha (2014) . Results: Health professionals with an age ≤ 31years and with specific training in psychological intervention were shown to have greater affirmative competence. 47.1% were shown to be competent professionals, 26.9% being highly competent and 26% incompetent. The health professionals with the highest competence were also the ones with the best health practices (66.7%). Conclusion: The results show the existence of a significant association between the socio-demographic variables and healthcare practices. They also show that the health professionals with the least competence also used inadequate healthcare practices for LGB clients. Training in affirmative competencies should provide for ethically guided therapeutic interventions which are culturally accessible and socially inclusive and thus ensuring the effectiveness of health systems.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".