Educational Determinants of Readiness to Practise with LGBTQ Clients: Social Work Students Speak Out
Bibliographic record
Abstract
As health and mental health providers are increasingly called to attend to lesbian, gay, bisexual, transgender and queer (LGBTQ) issues, it is critical for social work education to ensure the competency of students to deliver services to these populations. This North American online study investigated the self-assessed readiness of LGBTQ undergraduate and master's-level social work students (n = 1,018) to practise with LGBT clients, as well as their assessment of their non-LGBTQ peers. Participants were enrolled in Master of Social Work (MSW) (76.0 per cent) or Bachelor of Social Work (BSW) (24.0 per cent) programmes, representing 136 schools in fifty-two states/provinces. Students reported fairly low levels of self-assessed practise readiness with specific subpopulations (gay, lesbian, bisexual, transgender), with participants reporting the highest (somewhat prepared) self-assessed readiness with gay populations and the lowest (not well prepared) self-assessed readiness with transgender populations. Regression analyses were conducted separately for each subpopulation and revealed that across all groups higher readiness to practise was positively associated with implicit programme support for LGBTQ students, positive handling of LGBTQ issues in classrooms and explicit inclusion of LGBTQ content in courses. Implications for social work education are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".