A Framework for Enhancing Access to Equitable Home Care for 2SLGBTQ+ Communities
Bibliographic record
Abstract
Canadian, US, and UK public health and clinical research has identified barriers to health service access for Two-Spirit, lesbian, gay, bisexual, transgender, queer, non-binary, and intersex (2SLGBTQ+) communities. While offering important insight into the health service experiences of 2SLGBTQ+ communities, this body of research only recently, and still only minimally, reports on home care access experiences. Drawing on key findings from the 2SLGBTQ+ Home Care Access Project, a mixed-methods, Ontario-wide study, this paper animates an Access and Equity Framework, using participant stories and perspectives to underscore the relevance and effectiveness of the Framework as a tool to support systematic organizational assessment, evaluation, and implementation of access and equity strategies. Home care organizations can use this tool to assess their programs and services along a continuum of intentionally inviting, unintentionally inviting, unintentionally disinviting, and intentionally disinviting care for 2SLGBTQ+ people. To support this process, the framework includes six indicators of access to care: community engagement, leadership, environment, policies and processes, education and training, and programs and services.
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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".