A conceptualisation of equitable social work practice with transgender and gender diverse communities
Bibliographic record
Abstract
Abstract Although the experiences of transgender and gender diverse (TGD) people are increasingly recognised as relevant sites of inquiry in social work scholarship, empirically substantiated insights on equitable approaches to social work practice with TGD communities remain scant. In this qualitative study, we draw on semi-structured virtual interviews with TGD social service users in a Canadian province (n = 20), along with social workers in the same jurisdiction (n = 10), to generate knowledge on equitable social work practice with TGD populations. We rely on critical ecosystemic and intersectional lenses as guiding theoretical frameworks, together with constructivist approaches to grounded theory, to inform our analytical process. Our findings highlight that equitable social work practice with TGD communities may involve the following constituents: (1) accounting for social and historical context; (2) practising allyship by way of humility and reflexivity; (3) challenging cisnormativity interpersonally and organisationally and (4) promoting structural measures of trans inclusion to transform social work and social services. Drawing on our findings, we call on social work scholars, educators and practitioners to adopt various reflexive, relational, organisational and structural measures that promise to enhance social work’s contribution to greater equity and social justice for TGD communities.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.027 | 0.111 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".