Are the needs of racialized lesbian, gay, bisexual, transgender, and queer newcomers in Newfoundland and Labrador being met? Preliminary findings from a focus group discussion with Canadian stakeholders
Bibliographic record
Abstract
This qualitative case study explored whether the settlement and integration needs of racialized lesbian, gay, bisexual, transgender, and queer (LGBTQ) newcomers were being met. A total of eight stakeholders, representing either a settlement organization or an LGBTQ‐serving organization in Newfoundland and Labrador, Canada, participated in an invitation‐only focus group discussion. Informed by intersectionality theory and using thematic analysis of memos and triangulated arts‐informed visual recording, two overarching themes were identified: (i) challenges encountered in providing settlement and integration services; and (ii) solutions generated to improve their delivery. Preliminary findings suggested that the settlement needs of racialized LGBTQ newcomers were not being met; services were found to be sorely lacking; and settlement and LGBTQ providers lacked training, substantial knowledge, and resources to implement needed supports and services. However, stakeholders also envisioned the possibility of improved services for this population, such as adoption of an LGBTQ‐affirmative practice. Implications and recommendations for settlement practices, programs, and future research are discussed. Key Practitioner Message: • Settlement and LGBTQ service provider organizations must address current system inequities and commit to providing settlement and integration services to racialized LGBTQ newcomers in the province of Newfoundland and Labrador; • Interprofessional collaboration between settlement and LGBTQ service provider organizations can support the implementation of, and improve access to, culturally appropriate settlement services and programs for racialized LGBTQ newcomers; • Using data‐driven methodologies, racialized LGBTQ newcomers should be consulted on their settlement and integration needs, to align these needs with service delivery.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".