Clarifying contemporary conceptualizations of allyship with LGBTQ2S+ groups in the context of health care or health settings: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to better understand how allyship is defined in the literature from 1970 to the present with regard to lesbian, gay, bisexual, transgender, queer, two-spirit, and other (LGBTQ2S+) groups within health settings where English is the primary spoken language. INTRODUCTION: LGBTQ2S+ individuals experience health inequities rooted in discrimination. Activism to redress this discrimination in health settings is frequently termed allyship. Definitions of allyship, however, remain ambiguous. A clearer understanding of how allyship is defined and operationalized within health settings is integral to supporting the health of LGBTQ2S+ groups. INCLUSION CRITERIA: Literature in English from 1970 to the present that utilizes the concept of allyship within health care and/or health settings in relation to LGBTQ2S+ groups in Canada and the United States, Australia, New Zealand, and the United Kingdom will be included. METHODS: This scoping review will be conducted in accordance with the JBI methodology for scoping reviews. Databases to be searched will include MEDLINE (OVID), CINAHL (EBSCOhost), APA PsycINFO (EBSCOhost), LGBTQ+ Source (EBSCOhost), Scopus, and Web of Science, along with ProQuest Dissertations and Theses for gray literature. Two independent reviewers will screen titles, abstracts, and full-text articles; discrepancies will be resolved by consensus or with a third reviewer. Data will be extracted using an extraction tool developed by the research team. Findings will be presented in tabular/diagram format along with a narrative summary to highlight key themes that relate to contemporary conceptualizations of allyship with LGBTQ2S+ individuals/groups within health care settings and the implications for health professional practice and health outcomes. REVIEW REGISTRATION: Open Science Framework osf.io/2rek9.
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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 | 0.217 | 0.206 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".