A Scoping Review Examining Interventions Intended to Help LGBTQIA+ Youth Transition Out of Homelessness
Bibliographic record
Abstract
Objective: Youth self-identifying as lesbian, gay, bisexual, transgender, questioning/queer, intersex, asexual, and other identities (LGBTQIA+) are overrepresented among the homeless youth population in western countries. Although scholars have documented the situations and events contributing to disproportionately high rates of homelessness among LGBTQIA+ youth compared to cisgender peers, researchers have not as thoroughly examined the efficacy of services available to this group to assist their transition from homelessness to stable residency. The purpose of this scoping review was to examine the peer-reviewed literature to determine how many journal articles have been generated that addressed (a) the assessment of client satisfaction or (b) the effectiveness of intervention designed to help LGBTQIA+ homeless youth transition from homelessness to stable tenancy. Method: A database search of six peer-reviewed, health care publication indexes, with 50 key search terms was performed. The database search spanned publication years with no beginning year, but ended in January 2021. Results: Nineteen articles were identified that described programming, reported intervention outcomes, or outlined policies and recommendations intended to help LGBTQIA+ youth transition out of homelessness. Of the 19 studies, only five (26.31%) reported program evaluation (n=1, 5.26%), the assessment of intervention effectiveness (n=2, 10.52%), or an analysis of client satisfaction (n=2, 10.52%). Findings exemplify the dearth of scholarship and research examining this area of academic inquiry and public health need. Discussion: Without empirical research investigating service provision for the LGBTQIA+ homeless youth population, service providers have little data upon which to inform practice based on an understanding of the services that are needed and desired by this population to transition out of homelessness, the effectiveness and perceived satisfaction of those services, and whether services should be modified to better meet the needs of those for whom it was intended. Scholars and researchers are called upon to generate this needed public health research, which should include service recipient perspectives to target outcomes and interpret findings.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".