Psychologists’ Practices in Supporting LGBTI Clients’ Self-Advocacy Skills
Bibliographic record
Abstract
Globally, Canada has been a leading country incorporating the social justice framework in psychological theory and practice, which includes supporting sexual minorities. In the past decades, researchers and clinicians have focused on addressing systemic barriers by advocating for their clients. More recently, the concept of self-advocacy has been included in ethical guidelines, encouraging professionals to promote in their practice empowering clients to speak on their own behalf. The present study explored how Canadian psychologists promote client self-advocacy skills development using the enhanced critical incident technique (ECIT), an exploratory qualitative research method. Specifically, this study explored the factors that facilitate and hinder psychologists in supporting clients in developing self-advocacy and what factors they wish had been present as they engaged in this work. The sample consisted of 9 psychologists located in Alberta, British Columbia, Saskatchewan, New Brunswick, and Nova Scotia. Data analysis conducted using established ECIT protocols yielded 373 critical incidents (CIs) and Wish List items (WL) that were organized into the following categories: (1) 13 helping CIs; (2) 8 hindering CIs; and (3) 6 WL. Findings suggest that self-advocacy can be developed in one-on-one counselling settings and through the therapeutical process. Additionally, the findings suggest that creating safe spaces within the counselling settings and outside communities influences the ability of LGBTI clients to speak on their own behalf. Implications for researchers, psychologists and other mental health professionals are provided.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".