Bibliographic record
Abstract
The purpose of this study was to gain initial feedback from lesbian, gay, bisexual, trans, queer, two-spirit and other gender and sexual minority (LGBTQ2S+) counsellors, social workers and anti-violence workers on the Islands of Safety response-based model and whether it could be used in LGBTQ2S+ communities. Online, asynchronous focus groups were conducted over the course of a week in February 2016 with 26 counsellors, social workers, psychiatric nurses, anti-violence workers and outreach workers from across a spectrum of genders, sexualities and ethnicities. Results showed that resistance and dignity were the most embraced response-based ideas. Focusing on both responses and impacts of violence was important. Including family of choice, using the client’s language for their gender and sexuality and not making assumptions about family structure and the gender of the primary parent was important. Participants who stated that they knew about or were connected to Métis and Indigenous cultures and politics supported working to bridge differences in genders and sexualities across cultures without changing the Islands of Safety model. Non-Indigenous, non-Métis LGBTQ+ health care practitioners need more training around Métis and Indigenous views of gender and family structure to contextualize the traditional view of family used in the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".