Harnessing the Lived Experience of Transgender and Gender Diverse People as Practice Knowledge in Social Work: A Standpoint Analysis
Bibliographic record
Abstract
Transgender (trans) and gender diverse (TGD) people continue experiencing profound expressions of stigma and discrimination in their attempts at accessing care, including support from the social work profession. Incorporating the lived experience of TGD people as practice knowledge in social work may serve to enhance the profession's relationship with TGD communities and mitigate historical barriers of this population to relevant services. In this study, we draw on qualitative data based on individual interviews with 20 TGD people and 10 social workers in a Western Canadian province to explore the potential of leveraging the lived experience of TGD people as practice insight in social work. Our analysis, which is supported with the tenets of feminist standpoint theory, reveals that incorporating the lived experience of TGD people in social work as practice knowledge may inform and catalyze interventions that (1) validate TGD bodies, identities and experiences; (2) contribute to networks of advocacy and support founded on shared community knowledge and (3) promote resistance and transformation. In our discussion, we explore practical implications of our research for practice at multiple levels, including the potential of engaging TGD ‘peers’ with relevant lived experience in the direct delivery of certain psychosocial interventions.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".